{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SVHN dataset: download and pre-processing\n",
    "==========================\n",
    "\n",
    "A good description of the dataset is given on the official website:\n",
    "\n",
    "*SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data preprocessing and formatting. It can be seen as similar in flavor to MNIST (e.g., the images are of small cropped digits), but incorporates an order of magnitude more labeled data (over 600,000 digit images) and comes from a significantly harder, unsolved, real world problem (recognizing digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images.*\n",
    "\n",
    "The SVHN dataset can be downloaded from http://ufldl.stanford.edu/housenumbers/ The train file (in .mat format) has a total size of 174 MB, whereas the test file has a size of 64 MB. Here we are going to use only the MNIST-like 32-by-32 images centered around a single character. To load and use the MAT files we are gonna use Scipy.\n",
    "\n",
    "If you are lazy you can use the following snippet to download the files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def download_dataset(save_path, verbose=True):\n",
    "    import urllib\n",
    "    import os\n",
    "    if(os.path.isfile(save_path + \"train_32x32.mat\") == False):\n",
    "        if(verbose): print(\"Downloading train_32x32.mat...\")\n",
    "        urllib.urlretrieve (\"http://ufldl.stanford.edu/housenumbers/train_32x32.mat\", save_path + \"train_32x32.mat\")\n",
    "        if(verbose): print(\"Done!\")\n",
    "    if(os.path.isfile(save_path + \"test_32x32.mat\") == False):\n",
    "        if(verbose): print(\"Downloading test_32x32.mat...\")\n",
    "        urllib.urlretrieve (\"http://ufldl.stanford.edu/housenumbers/test_32x32.mat\", save_path + \"test_32x32.mat\")\n",
    "        if(verbose): print(\"Done!\")\n",
    "            \n",
    "download_dataset(save_path=\"./\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From MAT to TFRecord format\n",
    "-------------------------------------\n",
    "\n",
    "In this session I will show you how to convert the MAT file format used to store the dataset, to the TFRecord format used in tensorflow. First of all we need to import our modules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import scipy.io as sio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I will use Scipy to load the binary file in memory. It is necessary to have the MAT files in the same folder of this notebook. Loading the .mat files creates 2 variables: X which is a 4-D matrix containing the images, and y which is a vector of class labels. To access the images, X(:,:,:,i) gives the i-th 32-by-32 RGB image, with class label y(i). Using the method `type()` we can verify that bot features and labels are numpy arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'>\n",
      "(73257, 1)\n",
      "<type 'numpy.ndarray'>\n",
      "(73257, 3072)\n"
     ]
    }
   ],
   "source": [
    "train_mat = sio.loadmat('./train_32x32.mat')\n",
    "train_labels_array = train_mat[\"y\"]\n",
    "print(type(train_labels_array))\n",
    "print(train_labels_array.shape)\n",
    "train_images_array = train_mat[\"X\"]\n",
    "train_images_array = np.swapaxes(train_images_array, 2, 3) #[32, 32, 3, None] > [32, 32, None, 3]\n",
    "train_images_array = np.swapaxes(train_images_array, 1, 2) #[32, 32, None, 3] > [32, None, 32, 3]\n",
    "train_images_array = np.swapaxes(train_images_array, 0, 1) #[32, None, 32, 3] > [None, 32, 32, 3]\n",
    "train_images_array = np.reshape(train_images_array, [73257, 32*32*3])\n",
    "print(type(train_images_array))\n",
    "print(train_images_array.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'>\n",
      "(26032, 1)\n",
      "<type 'numpy.ndarray'>\n",
      "(26032, 3072)\n"
     ]
    }
   ],
   "source": [
    "test_mat = sio.loadmat('./test_32x32.mat')\n",
    "test_labels_array = test_mat[\"y\"]\n",
    "print(type(test_labels_array))\n",
    "print(test_labels_array.shape)\n",
    "test_images_array = test_mat[\"X\"]\n",
    "test_images_array = np.swapaxes(test_images_array, 2, 3) #[32, 32, 3, None] > [32, 32, None, 3]\n",
    "test_images_array = np.swapaxes(test_images_array, 1, 2) #[32, 32, None, 3] > [32, None, 32, 3]\n",
    "test_images_array = np.swapaxes(test_images_array, 0, 1) #[32, None, 32, 3] > [None, 32, 32, 3]\n",
    "test_images_array = np.reshape(test_images_array, [26032, 32*32*3])\n",
    "print(type(test_images_array))\n",
    "print(test_images_array.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can try to visualise an image from the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7c2048c810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7]\n"
     ]
    },
    {
     "data": {
      "image/png": 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sVCa1uWDYE3J9JNK5bZtLAL/H7hvO3DL/rkzy/UZcpqHUTBC2bw0se/b8vdbBNT1vRHfer/xKTL3VjtlqT0truDuOA8dx4Hbr6H38VMfy6Ae0K6JVHyM3FNCDu/kwAL+wfd7m0VdWwao6zWGlGztH0g3V1UKSuc5MbAxKVxoIjMnnZhCTRtlUnMwdRXJqnJGk/b3npXjOqiib9dmjgjIy07tVi7JJhmjH5HVdqDILSt4jd6ltWNziuut7l/TAeOh96FSZvjdeV2MARhw2k3hs1WGJK29JXX9Mnrx4P76B3JfTAJxHw+24od86tPe5nCDvAfbMhgafBptjfexri2Ef2/vCccgyq/iy1EJb44YVWSsW/DZBFyPus3mR2NXTNtNg674PlDRav/SZ7bwC/HFt7PP4pPuWuyr2lU8YMBnkvt7CCLDxCODnzOIyc2ALgdDvDfeVqxKoKs1dE+tlABDdjPzgHUuu6F998n1Y3cxUqWK5yd90mV2aV8BAZ8A/Ot/AizN+gP7I7H8M3/7WB/hvvUNvQ/qr2ocUgKRJMQp2SNX8iYBUiQBvhNoOkpvlEN3OsumuDW9feI/wMp4ZrA9wKRro/QcCPiuaom4clH5t1Jh897pg9SOZ5T07N8aFk077GIAOeEu/xGCs+mq1PaAqHY8ZA94awCbIs5qwbXVZ70svs7XyWz0RvwB0ZmHLjeEIJZi7Uz2LFqDvFdkSSIXlq8eWePntCCjyti6fCy8KfOvDN1lvwD+OA+dxot9u6DrAr7da8UNSsWQN4FOttaNioJeVyamKJHJnMmBW8KeBKua6hHakG3MweU3gtfWuOho3VdF7AL/3nAfDKJA6QDzHQJUYvgKfTyEburB9uoetSAHmWBorj3EULYN/Ds5itRNMZ2UWTJ7Wdypigt7mZoCUQmRDTmD3yS2sHYLyYwv+tGqgGqCv4E9KZrdeVAjFbCGGarR2BATL87L+Rkv9Ie8PBzwbgd47dAG9hownbFvliAamFfTBxqN7KZi8oHKyAIM/boSyIZn7pS5HI+Mq4UZBe9WfYLZPQhnYx7Kj96l2bD+rgA7ftjsH+Avw02ivAP44RkvNdXBnADz9YmCvwG+Q1peRmd0buDi+JPdJwtukLAb6xrMykZJoMz9D+puxpqJKzE6Mn5KTwe/lmLLM4n/N+Kxi8pKisZBPztca6TU9mYgC6CvzPxdeg9Q/EuC5UU+58jPw6w2XBKsb+6hAQJ+FaBXz0npOHyA1ENE5LO4XKzs25nMMTFFwznBzSyGepm5s3xn4s2HzjHUGfncl0AP4taIljZkZX3kfKYaqADh/2CAk2U1GYACzj6HXbbxZ2Zr6kOzsUigYICzn61J7qAabmKWJoGMwL3QqOq8AtarMNLCPX5NoRoxBkwxCbF/5+FwGu2/fxcO0rAvlcgZ/vOt11V0nEEq4G+QHwmth/AB/Br614i9sDyxqyBLZ2s76jpP8hR3/x5m7FsK9TDMDwjIxGvh4HQAaVDohpvTpk9Q30PvvtOU591kPR4Ddtk0BAKEgstwEsGN8CBkJLGzv+7d5GmnP8puHYNuLVfyClS6A99dNL4DvykHnIC0Vn5iloc1xAijlagnIu0yV5dTk34jLCv4lF0Qo33GxrhvgL7kJc3iqFJA7yiC7Kjnfshq4Di8C/BzWGCX/VXWCP7r0zn6idcF5Zr/RWYH6/cc6YH5BmvGn7SusiE4Dcj+eAFLmrudRQZLi9iWnsWtSOIPlO87zpPVOCqCvhuDsSCyj4+m5TUQoElRhNNyCrRHgNNE+zruxHXkaTE/DssXmVYj4WV5Y3FnOt8b327zP4cuezosXtJ5hvjuAELjnQRkyj9B1O0BnY5vuOAEuMDfC3CSO0G7ugmKS0rNifED2H+w2zzUsvgjwuRBqodQImt/rv1nBT+mQ1iH9hJw58wLwwSrA4Q050lpIRcmtzgPwgt7hlSjH/TJVd9at4UYDTPTrJO+D6QPopxkABn8xAudcmoRmwAf7o1RC2ydT6T8A/LLt7RnCAAvgJ6BKbCMBftzYDLS9XMWvaNuSX9M+0ivb06UQmqZNZEy+KhjDtnfF84wyGPGScrpGPmvkU/qtFjM0pTg3w3wScZBeAX5nDOpr3tZgifccPhDwReTnAfwyxgwZ76rqxy7O26xzQmurtf06unT0LhMQ56xwJ1U8JLBbSdiAnvHO/5yqS2cLs8Y8AKPSNh/Xz5nImRqFxEaLLexUH/AouITjCqMdU6IjDBtJ+fOk39M5gX8msPN6dYvGM6rMLJXDhviSMfK0c5sK1+W5Ho1vmVkDqJP1paVtztx4wripNAK5tLxt4z36eGuzHfzmpo3UjDK14droEj28OkvtyohvwM95lXbPDHNjC1zgnrV6BXwolHvAz+tX4M+G4NHwQRm/A/hGVf3/7p2UWZMTzSGDv2tH76OVuHfBOZn+lNMBb8HH8RfmVz1Gg5M2NB2WfLz1p6miiMT4/t5DeibZ94AES1J/JCkxhIFdJ+Ozv34m0J8T9OfK/ufpoD/PMwyl5xv7/rKAF8hj9QP0pE78XK7UE6RCjD8L1ypwkuaS51sA3TNXUCUXIb+y3ZrMth8D/oGjN+jRxstchw3cavOWDd5NNmdoguBiXMZS/S52x4E0iMyuMUOwvc9oV7LaIr4Sxx8HfolXAf/Get0NHxT4U1Q9c1KR+jVEBcw+cG8d0gWniIMeZ73HHvC23lob03Q1jaG9rc1Ir+P7g6EniLUagV3mFlZAtNw7gFjuUx89+/eV8Z+c9U8C/xkG4LQW/461318vQMzuERzUyUAhXxv3GJnggJ9Jt/J1oBfQNyHqXbJOk+8ect7Y/vRGYL31WbYNqgcOVegRSBRBbu1XmeMy4oMUj0BDrzY8Q0hdlTzeJDD9/J8Mpbmeu1sf21mNZvC/V9b/oMBXAP+zjE+V/Feq+oO7k1apf23FmLmkWwPOAD/OceXJOEsydwX/qESzkqCFf+Zh9ruPmfzQ2lQGxPaZ+UHxFzpO+5xGJYGK08b+vTXwnWc06j1N1n9KwD9xPnXfPmfL/wB9tPTbYCCL2ygo8XSHkeV4AezH1vNSPgj5+qNQqXEugz/aUSJ7aun7S1sMeHuZy9/dOELRaJsy+8BNATkYVjrmoMfo0XHwK65GbKdgamjdtxrIbFEfCKlBOUt91Vj3jOI4lGesYGfX4vnwQYH/jqr+ooh8OYAfE5GfVdWfuDo5/MFsDCxU0I8ZeaP1VmSC3ufuVFgj3hX4W9PJDOP9fhyz1NLjG8bAjPCNjfVXub/6XBF/LD5fZvr5M9af7Myt92c3qT/BfxL469J6AGjQjy8XAOf1YCyuzLEPFHcGv5WfIdjyJnXpSch1MwD52nwvey3bG/HoXQ57Yet2s67eDujNi1+OuFHEYbyTrZ3A/wAoKuhTvpUCjdWKegNgzhd3jYoCsDhVNRm3tX38HEnnZMn/WPhAwFfVX5zLXxKRPw3gYwAW4P/0//Zjvv4bPvLV+MhHvnpWjlYqSisS0TKEwWjsNo3BvK+kcw8/f3mPf1P2Ipj3CzcDjZ8niR2t0S+5AGx0bbGoi7mrNO6pzv77U73xrvcT/akvYH96ekr7vH9fdTECIw9QlhnU0bL/POgt7ZaPxvaR/5nlues1uQbIJBDTq00jrYqjjXLmMooyJgU5Dw5XcKrD3tEwXbTeBvg7phG4RkjxIMsxnu1IfA5EyPqh0amHEH8W5/5iexuTuL+5FSVyVk5/+XM/jp/63P+6TVsN7xv4IvKPAWiq+g9F5B8H8C8D+I93537DN/7rdF1LljksYu779X76ZPECmCp9dMEBWT56IQzGZ0OQMky8eOZzTGGMZ4i92us/Q8rkDh+3PY4vM/bwCoMvMa3mfvxe/fwK+iL/n4LxY4Rf9PNnaV+AzGDHagBg+9L1GgCeeWj5ByCPryjr1n2V+//NWIz2lXYMA2ANd8eAb4HBkHxuCCCA9fKcY3iw9A5BQ5ep73uM06/jGx4TxvT0ae1jVp0kCS+vqb9yRlrfKzTbQaQyHR7b//GPfz0+9vGv92u///v+k8s4fRDG/xCAPz39+xuAP66qf377kFs8hoFff9wiLNYaLJyvBv4+LDiAnmT7PA5jewb9fP6sSOYTgt0JNyrsOujMXnEbYNNcDdBHJXBA+/rGBjj4NY25TyP3yNdPrfxPBP7zxPn0dHd0n9bnMuhBICe5n8G+GoFZiLtV+sgpAZ7Ll8CegT9nWdLRWOfrzqrwesBu3zDYA/Sngb8b8OMaFUzVRsOyK3eyQbsILuwq+J+5SuSebbgGvUeOyszaGmJbl7J6JLxv4Kvq3wbw0UfOffWKH7MHPZBHbXEBzycOtp3A6tpHm5xlnDLoraJucsIrkYF+fMVHZagIJWZgEDP78wshMZ6aZHApvGBV2p5v2MWLORr9871HQ9/TbNB7yuA3yd+1juiL7ZXxucIQ4890sgHw8/36kpdFckMC0CvAZTUK8xxTedY24+DHWDcD7ca6Al9kDOqaYz1szMcwysOYi852o5k4jQRE/fL9cCNQsapGDpQFti/frIaZCqmy/w7ozbWwukNlZmUTrlqQ3T1XhsOLjNzLjB+ZYP7aTv5FRYr8zJYO6DjHmzjQ+Kez1Xd2+ZS8HKP+MHsJROKTXlY5VWc/P3LGkiweBS3pw5yTBBaGB2+zQTF1Ufrz3cenrrvzHAzvP2f+yvjZCARD0PNtH6Unux8R4SvJv3dHN2CvxoDkP7cFtCOX26FzKuVDDe2ElfzweFaHSJ8fVe2+z9y3MfW3+qAe/0Kgrq7LyCfJj/JzJtC9UuoSp5ov4zIG/3LTBfQWouzYcIdi1Fqn3iTgr4w/lgAoIyKj3TCm4+oLhdqrd+hin9tCyiDOBH+CCE7zD6cBiG/5tfE9PtH5eS4l0BPrDcxPyZhlvgg9H6DrIrBBScOSCbBnlflnZf0nZ37rCdgZAAZ6kogkDbO8vwL7RkZegaKCf+a7j+F3AxAt/4c24Nj3zli5ZZaHA3uM2T8x5m3sOLugdQ33Tfr4JBvdexTTJBfLI5HyOjZLu9gd52rI/jUjUp6sGcZsfw16e2IRkEkxpjEck0weCa+J8X0rn5g2IwFJjtkUzhgzr5pvDjXA9vHxTQY+GRERe9lnVIzeR1de6x3qb5Gx8VgZz0A+2H8UjTUebaJPALPted/6Ki6/juv99tR3/xRs//T0NFg/gT0DPxrsRoRUI15r2h4D/WW1SnnMDIdg9k3vDb+5dxwNPOwaOMiXd8GclYR3GZ5oTZLUHwpO5gdWc/sGYB8fmYSTWLzWxSWZyb+vjZ7L58klA53vdsn0lgVOZmGk62CtzuM43iTG/7Iv+7IHzgrJPkLIGj4jr0QYhkB9ksuREZiVofuydwN8T8dU21xvfs5ORnHGCuKLMQx+JXatqOHWc16CrHg2PJoKm3/LvAWlgkQlv5PrkmelSW0VltmXk/avZWFumis4Zrwq25eCtDhQJS95we83cGXPLz2N8jMF15s6+HtXei9j1rlB3RPAAEO4KtMkCnLCKRX7dNWrvC6pOx50TC7KHzRac31jk7tA74WXkfpf9mp/YGESA1YALDXSmSyzK6qMJod2gH6M989LA3rsY9BfgZ3js7A7phFwdiRQW+LInnn0CfypLcH6+YsP12sFSEhHXr+T1x5j4fRkV3ViwbStg1+A3Sfc5i3F8Swmped+9s/F/pkySIy4lqklqytm+0tlup6X2tFU0LVBdLyr0VXpWlBZDpAZafjnvxBDfu5oVE/32iBQQV6PK+2rRppA32t6Nyqx/B4JL8T4Bfgkt4AsgQe4LbGdDAHomN0mgO5vnAEYzf1D9skW/AZ0Xm8zc1cDYHHL7sNkysn6AeiIq6eNjRizfNpH6X+A7Vemn+uUL5Y1u2CNkSJRwYPfyrmA+8Sgc3cnVuDbs9jfT4aArqnBbH7Uhx27ax4IZftkjP7Mim9+sWkqQTcCMpYM+pQZC2EXKW+JXPd6Oiy/U9qSG0Wy38pxMXBRH/xNzTlfBb+2/Uh4ceDqLECkAAAgAElEQVQnkMMqJ4OrE7OtclvIMFj5jPuYlSZQAdN377PQ89LAb1/reS+sD8ARYM+N5xv4ESqFkZJYngFPAO552VM8sK5zBitSPkRkS/D42+EN9Fn1rpcutxb6s2JhgD410mHuWxiSljVfGoHBJK8NXLLtJoPp+3j/Ii9BeWdsH2UYCZYU9ZTYxVqt+etfsXcjG+plSWMx1grEexfpPYzo/THA29Bte4X7kfBCUp98fJdaMxMQFVVTYQ7g23BUA6YDqq/fHXMGnv8EQ+713iFt59fnmX7ugd7uz1LfgQ4EEhSUPrJAgFfge3I/g/px5gcxfnpmXSfEWt1VP7T3Re/tqswvbkDiJGN678Itfr+kizNTWj5avnQ1f32otTQOQmPZulJ5t/nyVc1jW1oD8fT5Z1zWJFt8ScrYsmSEUn6GWxg5Vhk/1E1k3yhz8+Pn+sSBvcbdqcvXDMAj4WUY/xUx/ka2W8UdgB8F1dtYHyAzCdPc+o2CMeMBSMrQ+SxVNJnWvzevLCHxgwnuS+nVCFioRoArV4qPxs8VS2KzmSedKmTppqmGoYLC75Ey4V7JCP2NXflzzvdDBX901bJ1Qch7UBcf+/gXDzSGzOAfr2uHDI43Ep3557wLXL6xHoBX/2eQv067py25MsUIbHPI0mIqwsoOC8s7GaASQIA+zcTEXb7z90h48ca9faKM0biRIvrYw4jZyznFr0bi/bTo2uckDqvcN8Zff9EyvAM8s34NDHSNWruCsTA7qGKD9vOkmvUX3TdZRW0yJQfJgF+6n/Zcd307O3ZR96Mhnxh/LqOrbg84A2Q2qqwAqc5MaW/HjOl5/dJVumyxnDni7B6gNzclciHi7HnrGbPJPScBW4crRivfOkMTT8129tMHd/Ebm4+EFxrAk4GvGgWJUpDjd6Kf1ic7gH92GeOwT5aMwBy3a3wfGRgifLoTufLkkW65kehed17XvlZR85X5GopLsD8duwh5FCNPQRWTTMYXhg9nAntpqLU6kMNiWyooS9ast3O6tr7r/cA5b6HNUXtpxF56fbfRORIf5pA6zJfcgcqy5Bp4TMq6M73XB9lctyZdNcomnwRvIH02Y1AJL4AfRj/UDQ/hHusxaYu9q8GvbT/N7UfCi4/cuyehHfRdxuSacwy2nEjsYA1RIZHUu5p18leUIXGG7p5PTO8DPXbnCLpN72zBG8ckVfYMfjI4pEa2BoAqUm4EG8AQnrb6iOnERGe8J9u1ue1SnsDiQ2Ek9mylazIGvD/Cvp5bqjL4eZy+D7pZBuHY/hbDe9ucN1Fa7gwo8fcnFSMb63W/hBpDGAPLB7vXpYIvuSJAfKbtIm+yGtypjgl4786NBjt/d6MAPyZseXqzGb87kHbAH0x/9tMl/jACmO9b810N+BqsSoXrZyn/ro3OYgTm21wGdnvZQ/WiZOc+VjNJxpsC0F2ViFsYRhPojfG1jcEoE/THwc9BGWo87mh+KcvSaElHWWbwC62DAYGc/sXNorKx7d0Y/uWFHUqvbdvcDPl6Zv21TlS3zJYBemJ+O0cVsO48t5nr+AYuq/p8Bn8O5bmlfgyFFvJep2JbGvDcrzeWf0psb/seCS8zZPeS8XtKfD8FXeabVnIOeX+eEwiSZt6JzGsJ9YltfH/8lgy3Z5d1oV81EBYDB0qo6qhEXMgzSsGFZAgW1vfhLf5qcusyu6fERxm2o8THWM0N4fyQyARJejnKWJ7X7Rw3FMj+a9G+tW4r0a0pHV9HxCW68MLnz+PvsRgHf03blRCdt9S2DHRbZylfFRlI+o8R4TLZ3upRVT9LkYWNuxeYmLhO9QA891DwJKtnar3vzvAMeFt/JLwGH58+DFmY/2yjgst5zgkVzsn2hnld/5k8UowJFwDkwTxwIFjhdu1jDLeP8uIRX3s1UoFfAR8qPuI00ltUAHRFjd2DXNfEcJsW6kMVOOw5/LxYDzALAU/yfmtcEz53RsjATu0OtYLzpgGM1z29JM2FcBRtGpHutD1n4M1sbxlW1zgekfeWP14WNkSWy3XKmTF6b9K8eQSbZyx7LupDzStTYxaXGJ+R25uU2J2nYfO3Ne1FLQL8MAZvEOO/opd0qtTvvt5jBhXJM+qaxFfMaTW4wJp178ArLbf4m1VP4H/2F1I/fvnFnxryOH1mnA0zMzPmu6RW7ypvB9vPuCTVgMwmcbdoH3DQ1IlQynNA4HK/g9sAyvMo9iHvV7Zd8quur3ZmyH2ba3+J716Ca8qHDHiW3DAD4HJsHPOpuFXpK8sD+tUA1HXdLEvmeNz839Jqr/7VJPPZn8qU6+zXPz09LeB/JLxGqd/DCMwRSSLneNuWftxW4XDRw8ddjym4x9z4eqG5AvyPAv+OMcAwAIvMBzB8wmD2K/BbPqT4IXzqCsYV8KEsSkLp/iDA1OXaUr740Avo5epRYXxceaxpzxfnfOBbu7weuTniTBN3pv5/UDyXSCHFZSv1fX8omQF+xKQbO3fCHpkUCEIllIgMMjCmJxLibtoEev6oCr2ZeT5N9n/KwH96wrtPT97Y90h48U9osR5yG9owPow4f6KNJsQIZWDjra0i2LrAXtEt9YBBCGuZHzPTjBlbzjmQx5bRJ+yNTBQfgaJppwKOZ8UqAZ3dkfKv5o/510It3OODEs3lJgRzivFZ+RvFA2sFrYBPzNnaFvyNMpGZPxrUKPbFa9kZOje65oKQUTTWtbtItKrBeJOZfVUq5bdpBFwbBS09xZbd8eEtbRyzktORZsoXNya9vkmXB+LwtOrRgEeNeE/nMt36k7foP4W//6a16meyoIIuYmlUUJ7b3oAXDVze2CUCaQPAVhFy4RHIEmuPAukKdBV0Peeyp5+oQLq9L96d6b0CXFSUAHYBfWjOkX5RYg6QtDe/1uaZb2FjXBFInlvO85XyEgT8JJcbdZMx89fPiom7TmwIknthaa5MmoBNed/Jp6Wh2LtcrE7z1i1ZfqBzMOoHixf7WX6ZsRXKvaVcFdy4p2qiv7r05FZYnsz1dXq0zOx5tqU8Gi8mWH1awW/Mz99deGOBLxgZOes+/CeZ9ZPEFfTW4m0r6+JJoJe4v7NSVL7OwJtDAfPrjJn1xbrP5sQeTeNlnpK6ZdUKP7kXySQgVx2usDRgpx8NbX7dhyu4D2bqLSoxsrISrIDn7ZjhmKY4N5UDA70Bg2W1pctWIyVhXDeuEuX1+Cai9Vf34p1Nf1pNBVTAFzeoLJ9zX5JCwE4nrUExh2RPt8Q+iOpVTCJPvJ1HwxjsWD6NxOvRcPdEH02x6dWeqgGgUXq73yPhZT6TfZG3QkvFmPRSWjEA1I3l8p4LtGF+Bbcqi1kAwqw/Klmf2rmyPP+GwhjvcquDfjB/ToGmtCS2L4yfoR+3MFZ1Y2aj9FShR3N1EGkWN1IB0nEzVwUMfBoZx4zfLo0BgZ9Uxqj1GfRh4Arw3X+lMeb+AVC7n61beYnfN577PPh3hiCvw1xyRjz9aF+qQVSuSvMWhCnwc7oDvhqAmXa9AD4B1j6ZlpidGu3yCD0ettud8d+ol3RElj3+12EgmF1sArUpk6xBq+n4RLYxPf96KcgUohAgPOGGzE9Vl0kMdLK+dnTr7ptv7XU1I8CgrX6fiz4CSJG/rEbI8hmuRtzYAA73R0SgMvOAXjQKkE64kyxPkn6zHMCPD1yacTC2r3LfKr4ZVTeuJv+XBttYP/uJs53oJ/xzaKbIAPj9uBBHfjE9IKWPDeF2fWswyJBsqo0Am3oUZSzTQNkQaddu1I4R7VIEfGb8M7b9y0mLD3+mlvpqBLwtIH1JeUzF9kh4rT6+091EfxMCvQi0WSNfDOQYP5QCFfNq3X8I/z4qZsf4wkrXhtb7eAPQXgP297htaWDvAXoDr3kVqj4bjTjoEc+/aODzyj5uQqxjgJ0f+rR/fYzF700g3pc/Xkjh9HMDVmJ4XjLQ7eMlzP6tEdtnuS+gkW4J9NPA9gB+nTyiPY2ye4IAeEK4YSH1h6DIrlRl/LHveVm/NQCmILjuWbVZ4T/KSKn+6mR6UZRoJremK9KblavMZ0PAH0PNo/Dy8glP78Y+uzbNuTgNwCPhxaU+e6Gz3yT2GOhtdNq0rib5+TekfzAlu6CrZIvWqA5AegdaHsDjEl/PMWXTfKOrt9y7wPey8QLe6BVPi7+pYTEMR5X8XsEtDxBdhzoB4WPy533GbLIXHyhBTGrJkj8xfgvQ+7mpzz8DxzI2N+DB143dguVivRFwHfTN3raM5hcvMyVffCv3r3/2ss963YwD3TNVR+FKtMbFy9ckami3ZcIUX/aNskxv2fX0ll100Z14evcpbXuXnQN/bS/o28bSNbwM8LchgOJ+LiKDY7kWrCPdpa2dH3xroMvPi+tsBp6rfntqdfSft+o7CLI0tQrMR22DKy1gUz/FwKDwcfMnpY0FWUKO0y0usoIfc18BejIAsgLf/f6pQKrcH1LfQM/Mb5V8GrXeZw5M1YTZIEtA3rtm98O1hJf8yS7/Ra+G/yTyJAxgve/6W0PUrcru2a9XetkmuupsnzH5u6lPPkAf31R4Sqog5h9gNfl4eA3AX5HhECKwI20jVRRSxs8UzJoVmVX2oH/eGNQH7vZFnH0g4c5FmQwFtCW2dpy7vXJ/OBycK/NXqU8V3qa2LkxvRoClvcvgEpf0mwwuMibI6DAD1gGaVyGBvuTeknVsRCXXhUsFUOW9g326NyUvAvC8vjMwHB9uk4g6lbvslBrzYpqs+oadAf/dwuoxIq9+Kv3JG/Sit4TqxHsIzwJfRH4IwL8G4Auq+jVz368D8CcBfATAzwP4FlX95eu71FjZRw10Jc7E+JLZB1EYqWVG2F9jwK8ZwhXqXvfTyvg1PRdgn3uXVzQtiqVyDdDbr22N2qhv06I705oEpcpbmS6BnVv4JbF9UgAEfM9/UlZXwF+7OQfrq5dbSGlL405VrXme1d1W2hcjygogXvLJvRv7z3nn9HqcN3WHDYD3YvhAnTnkdhmgE2/Z2Vt375If/+67zPqz5Z4/mDpB389ztrXA3a1c+54PjzD+DwP4zwH8Mdr3XQD+gqp+WkS+E8B3z33PhipIBkjiVUhX0c4OVFEI6PnfqhyzLMfWIu787ypfw5peMf7kXd7FoCfWj3SN81ubwNCGMdf7mF6M0z9G5wXoIy3mCuQvDq/Aj+0ARW3Jb8SGjRRXXsIZv3bZEfB9XPsomWZj36dx3jH+ElgNFqZfDcBeQcUrvo2MQNvnBxtIWdNt+8O4sfoyt6fTl4qznI8ut/1bdpn1n/Duu/x+PTf8WRde92h4jEp+PReeBb6q/oSIfKTs/mYA3zDX/yiAv4i7wN/pEE0FPECvUTlcZsJL3VkQ1RDEXWXDGtY4k631XrYy6Kt/n++3Z/30nGIAOO7MVphdeA0NEHv3AHO8eBRyykkvaPZly+g8yUBnOex+vbE/Mb8ZW5f5M2ECTF925l3Lo/EG6NuMnuXxGI49biueB7Vu3FOqUZlZel+zfvX7+evL/Dn21d9nlcOGJscnt7cQeXjr+kmNdwXoBcyrfx9LVgk8jLefnTLlank/vF8f/ytU9QsjE/TzIvIVD1/J1AUAoskH9t2UjgA8UyHvt2tXSb6Cno4mpu+ooM9sz0x7LfM9/hwb37ionBBgTgYqOr7rPjo8NM/4szwhA7+O0sugKPt84A63BTQaq09Gl9a1K8TzTdClxwQlbiBjAJY1YrqLZjcTfjmHpP1UR7W4rtid97ear8z6JOt5+q/K/PYsSnpa37mB3IthXWz1g6dJtpfXa0cDXwb9kwM/uwvW9y8knxZl9kD4YjXuPdS0sNr2KGxDJtW7VBh2XrX4vt/u4/dd/fyQa+PEe414avIalf0fDMb2ZHQGi4eEH4AfjV9jrvf55t8E1QB+LwWaCzd3wZWXcu68tFJZPg/vjWfUyqRtjKuwHhHLU5sFOcDQ3Dh4mVH3q1je7uqDP5vLegP+zYAuB3dlfTJukrZ5GT7+eP5a3lGehe1VSerHuPmnCngaW//0tEp99vO5J4DH9/du7uWI+66cnjMC7xf4XxCRD6nqF0TkwwD+wb2Tf+A/+xRGVgEf/7p38PFPvAOoJlZkwNt2qEwqlJIeBn8NCpsoWvPNPTZrmwMfEzZM6aHX8VTEeG5720zEvuE3lxA0HUOH2/Tpr4yQtWHUhzp53nn1lqX9lSxO899NkNRnxOMF6EppDOCLWG5O4KPH79kGQU2PUctLjfxb2zFIxl+NZVi6+MzVGrm6Mya7MvZawRKfBygR2Bn053mi87j6pxX8PEnmadfTGIjUaCqzXKSW+zDcP/+3fg5/+2/97BrxTXgU+BVyfwbA7wHwKQC/G8CP3rv43//93zEyzv9qvhunjYEEMgiz4gmfw9LRb7BWpvygqSWT2bH4PMPobGM28WTl4dIYCmkAukCaAl2SERjkwZN8VIDwo4WeuUr9LfBrxZ8Zup34spFsTGkuqgtTuUx3ZKyQ9IUaSogRu7tTdszrgksihEqaacygLwaNRyIuRmD/2nEQyH1pvw+RHu+zvzMoJ43Df9qD30DfC+gH8Gv2BPmFQovu2H/mq/9ZfPVv/hqP7f/yY//DZUoe6c77EQDfCODXi8jfAfC9AP4QgP9eRH4vgF8A8C3PZdjl/ecf9vEXcAvvi4qZQIZScGJPtq+/Bv/fT/B1HDe3z3EUQOcrnAIdDfR9ntkwX/wBoA0N890B+0Q3AJ77bRS6MagQ/kgBXDChlLYEZwnzB4Um6UisiNnAdSeM9keIKI1cRLwMNWvqYPr1ewXjRSdyq2rji4HfkrhjcQd7HaDTtn77vn2AWN7JRZb4KP1ZGvNKS3685ZlB733wBfxj+qzcam9ynj+gEnkzy566X9Ow62kAHgmPtOr/rotDv+OhJwBbMFVbEEAS/3klduQHkyb2vXrGrhGO2V7qubQUYqEaUQJ6lUJSImQSGMAcJgxoB3ob1q4poBIv/xgY1Nb9vvEUyw8B5VP98bEZEaFr1lZ+YsMaeF9PRTQMQGoDGfIebsAC9CDwJ3VQs5nTWkHv6zEzT2X2e20byQACKU+Gkpl1I9VP9UWS+vzBi9kvb8touX9KPn56rZbdAO8NmKw/J4ypSt/imYZct4bWDl9/JLzQyL0oXE/HjllFMHqAV0aNCmz1glC3gFPLc1ja15OrAdjH/TKwEXCVQYcbYPMPTFzMGYfsdd+pfCETLCZ/rO9ew9Kb1GOZ7yxegQ8HO1/HFX/XBuA+fskDN4uc54JhIPnHPr5OA1AYH8z21u1Ss1Vi+azvTirgnotzf3vmFwrLGuZJiRn4O3fh8bv1O8Z/IsY/bdAO9c/7tX0OBDLHWKJCcblamg30R8Mxwf9IeI1j9S9CZVQCVFRkstTzIuFrUyCJL7Evm6FnGvHq8iK+WYVcWJNo+AbmlF7cgxBkEywDSmv2S6MSo8h7V0Z8De2rwEmuAcXFUjRySxPTC72WzAwe/6xxjxr6yj9/TjJo8HgzMLkBMtieejWumH47HDcIJAzotEPgT7SFFXA15qAvU2g588f78WmorbH/UwC/juNPn7smg5+NPr1dOWdpOtqB43jDge/M+AyZ1msyeyGAdgH4VcoT699j+TvH2BjJRnQk4CzXCpjouI3ryliMFvNwd9ISmbl4O8clr8d5rQCCydfyiNKhO2M8DABZM2BKfWP7vrB9xxxkTnl1BfqNcfK++HUizvjUWO7aWz7F5YYyF6BI/tS6lYENjWWpb5Kcu/B6j9Z5Z3oC/5N/BCNG5vkQX3pP34fiUr0P404Dr46GY35O7TgG+z8SXui1XPJVwZUx9o5tTRWLLTTSqZIqyGNxoJWqIKgCxFLKku9R7kvXel32x5AMoDEEEMzG8N3HN4llMCupxZeXoPgXOR/n5LhS0im/BTbZhwhKxa+6d/rxpQcCyZ/PDV9jVFv4rrlLL0ah5TRxPBnw0ahXx9lHA1eW8W6hOR2SNy0C23EdDvSTAM597GWgDYG/Mrl6fsyxDwZwXcuhdtnFQKwB9nbcJuAPZ/zjeJOAn0L1s7k2Iu8nOZ7larmuBits26zGwlkTGUyLESjgt4sqzdfHCwAbo76kd/rshi1JdtETQHVxD/bFGHC6ct6IPZe2kPatT8+fgiJXBAR0WCNdMQSd/F9jxNrCb/37UE9XjkMBfWH/5a3C0qK/vGpr5bIpN2/BMEanacMWhk8j9E5/lz6B/cxG4erd+Tw03Dl9DuPWcGNotqTRDmMMf0M7jgX8j4TXKvWX/SCGr7PQ0oVhJyoQd9RMjLtQHgrYLxQAkLbTY9wSMbMjGF0kg9/2z5vyLD45L8hw2bkedYlzOH4GoSuDCCC1ZzgisLE+SnaXJXn48kjgWLvvup6D6fXcgF8d/CM+mtk5gR4h10nGM8jtG3u8vrRdLBkzO0upOaW+sOUATZNjRj99lfed+/AL6NV/WgwMlfuMYwP58RKt9jIb85jh6++R8Jrm3KsnzD8E+EysmaH9GmbwBJXNvdNz6F5kNBxgV9sJ5OL/OLIr2Osy4mUTEPl4/EuDSEzuSbgA+SLxdbs6ostgJ2NbGrUY+LmvPiR+yP6Yxsz9+87GwCaPYMYXat0f+cQulvv49P48M70P1xUyAGKNgNlIR37NJ8/njlGSuZ++70bn9cLqdZKNNAgnDEOaZXjmyWjnYDKLuqliXXbHdmmM/0YDfxuYoXgXH3d6jQa5LfvJxT12zyIjwy7AfeavRoB+6ZkjrknGJwnAYC/LTVx9l8UzNlJFYZDu0u84pkoPv6fm+M9jYj0NAmqDILYHdcsV/74z4Huf3y44iwEgkEnMWRttFVa2mblTox29dBMDWpjxscj9nEnTgZk2J7rqQorbYJql5b5HV52vd2b9ndQ3FUGMT3mfVBvCnRmt9Ycvm8n6Wyxv/0gAX0oFJRZ3Cx/UQ6cwwguzsVXHCoBFChdgOxssDB/nk83IlWhJD7FzQpzVMJARMHUwLsy4r6wueR1XQemvsdo8knSlRtq3/r5ulgX8aSx+lv0+XbkbgGkMLrr2qusSvwl6BzGPVCtMX1wB7rXYldUoCpLb5Ip4vHt5Aaew+nnmRryzNgCS1A/Jr1EWXqiz3jdx4B/ekBfAHkbgtgD+zQX+BiDLCcw6wpWemE/Sju1zXDEs+43to4axUYmKNne5UaClnS7l3t4QOXap0n3JABi7WH6o0n2R1+v2vheDjeSsyqpLBYfWV2HpOk9P5H/ubrPW/vkrsvja159AIWPA15nbkGZOorxP3Y0X79Mz0y8TbTQuO+RnpNTBFUwyXtZA6d8EMHlvTE9Dbn3wTjEKZwY/G0mPSlKVYdyO48gNeFPe3257mf+GAZ9aTZmpa4N3vUoJPIh6mJey/lQQLWax33SEBSHQL/3hID8TXHEqECMxK2DT1vSL6R5zezfARsoNLreV2d2jQuvqoB+H1IVHumQ244fR0rRk8LsBAIGVj9YW8WU71vt87dg0xNK2QMZ+gJkUQH0dt+435UiE49pLo05G/dxNg90z2NNoPAK2L6u0n3nvycr1y2c88oFI0YXnPvzthpv78zccd0D/hgGfgmKMirorVYlsJgNVsBe3M/8ohP9uPwK5/WNWQRiEdKw4EFdi424QCd+ZtutNFwNwZzsRNLBU5Lky/ypnLBnOPHNw5DU16vl9TepX4R993qEGHgN/6i/XXIgu3Ehx1ffvHUAtN+6lQUkEfjN+Fk/LDFWsYPcx9D1esX2KufDDENg10S7ArfZcLAJTnQH2UCrRkm+gP44bbjcGfQV++0cA+IAzvQLxzgwf3rmbdoFdP5cXeEdVAbt/YQhIDxjr+/55LyEj4vcvCXok2D2tvhlrsxLi9d02uBJzuPDTrdYlFhfERBMMeCn3YdDTYwzk9Au23zN/nXM+uwfxbYLE/C77M9NXhmeWz6BflV4CfTE8C8NP4PMXb7qzfSfWDz/fXt7hLjsGfTB9j8+Z8ZeN2gGRNuT8jaX9LbF/K6BvbxrwN6SWwX9x7sJAdClUvD6HJ7G84rMP7EMS68c+EPiLAUBp9OM0gYFUHjnxpAVYi4S/499vzy9P9Ts7yEe8snSPK4LVLV0Z6HmdQL35x+Bf/eW1PaAT6F0lcIGSPjejG7P4SG7BF3tVdW67e1DVWSiSHEf1mXJ53rzubL5OrOHHnmh8voO/Z9AnozrqWJM6AGntsrux1L/dwhjYizkM+nnNI+HFGd/Gnued2GI1fHx4xlm9dRLzk0kReCiMXyU++Y7JEBj46T5JNj6fSr86nS9T4diKMgTj5Et5X24Y1ShLyTjGmYUkw+N22RDV78HF/qwC/FcYf2H+S2m/kfphwXOezXTXobu7/nufRchUnBtnYv2pWALwdZAOgz4Azr595yVLfRp738e32KdBs3QZsYyoGNMfDPrZcn8zoB8BeNtnQG8O/HmPNxX4AMFiSlyvvBu252uKAoW9sqg89M3PYY4GoykqkIF+YwiW8d4prKy/0S6rgUvJmpXTEr0B/Q7wq+S3/m+SyNUlMpZzuV8cBAK7RXn17+M+Ce8bxndQLQZgBXtSEIq0zWkP9s7g5xl03U9ud9wmyiMGvffbO4BjZJ7Pbvt0PtPA14t/b+mFV25JcYoJNWx0HvfZW1fdjQB/bIFPr+a+ycCHIg9aQQV93k5SX6nHmRRBnB9cHbzNrD/Poha/qFA70O9+7z/pZoRCYa+Afk7uP3d/l9tWwYFg+3leZv0cg32jXpb6pF/jGAqYL6X+hSEo90wSX7BMqJnYn/z9xO6usjyilrIwUEsfO43Em6A+6+QZxPY8G27uuiPQJ7kfaoSlvg/DbbNRzwG/gt9YfhiJAP4b/VouAAc/NKpcyC3E7CYnLX1ElOZW1DOkVfeGFaB3xfjs8yx8Y3da5p91rWydwyUsHkt1Y6ai2V67bLMllFWlXGZjaTUgOTk0yKzoIjMPRsaHi1PywBNm1lRhw0EAACAASURBVFmnsdUMKBjrG3BjvTOYUgNeUQYpL4sBbjuD7EUIj2ZSXmw4Nm6D7XDVkw0NG03+2TDbPK+efb9+Ln3Ibl1GPGxkhXO/IuZnqA0ayT3lKcbyh1Ab7RvtAm8Q8Gul1bKiVkU7/BPDCcinfY5Iybqqf4ropG+WjYIK8KsG6MdTVtDneZ/vs70JCOFalxIXYB84my7AFrmFmbhV4UGWJ5Ge5f7uKWENtgrHJKjMZNj5Nr+/lVM8YgN4WvcPO1LZKIPe8zLSbKBfZ9OB578gQE86hZYZ/NkJMyWka0VM6aIGP/b/qc/eQM9TYeflICiz4lITjNm4SmrVIe+GeD+RavQAxJuJb+DUWzl4xZqB5acWwEe/qrG8ktXlBpUV8OEzMuhnDAjwOVPXWWrDKLgdvkybgz0B1+RN5AAf3gO+sv7+Ye4xqNJ6VHbrLnSvchJNBRQzvvIDJjP5Z878SLgBns+e/8HywwDkt/KYbS2NweYUrzLeHlLi7dcS0C1OnnWV8gvbk1uT7qEZ/Dzk9kzSf3wMY2F6I6T51RurW7A6ZmVLj3UnQJjtA+j8hmK4PBP0R3vzgJ8hINnATmoZGYz5U+9aOU/FeYacP+e+Po3AmV6cYPDbT4qio4zdyn0CPyRXRKcbbADpsLOEjXQJV8AVxRLFTcwAZ9+rp6wStayb5Lfx+PT8ga8dmMoz7UbG/Intx0qW+6y44q229JktNxgEXCTBtbI+d+GFEg7wz7h4PqcGo8r6O3egKoR9o+QgI2rcc8aPF3POAv7eO0l1nevzQyiIGZksR5IixUpC/E6+T0hi7QTH+D0SXpzxo2KqG4FpYL0SVX99jKAixj9zBvN5IfdDzalX2qg1IW2v56Rn2W81NPH9lo1HuqISJmpPlwULC29NNRAMnZidIe7Hpthn1UgPrfMB2DPvMX6ylVeSeAP6Chhn/B4NeX6xg37Nb2+4W+Q+q4OarBxzTflFUd/4+D58iJFYDRnJeG/om4zfCfT8Oezz7PE+gQJN7KMqisbzL6ZakUmpFTVq7+XnCTffeMbPG6pmYYWkIrLcLwzv/aQnZ7J94IAZn0FvcYmaI1yDCvgz6AOcRk8Z/JXNeZsMQcJf3DcbADpqQDRmU67KnJMk0eNPSvW8nW/u/Gcxo0OMWWGzlmVuwe/828w24+XNN2QG91+APy1dpdhlc86/Zd4/bGJKBqKqgrppRmrn45cGPv/OHbmgzPqjXx44GoBjJLqBJ/S0KLpVvsv0QpL/jQZ+BFky2NWW+eYdCcDcUBJdLBoZbJlMgybSXI6pLoSs3n19xg0CfY8OtbIJFc6dlDLvR5XLBsIs+7gdsb7fm//O7FODfn4DLxtUY3l7CgCbEdfSAhvoIsG6rjZSicVtq0Eg4+qNeebf9+zXe8Ofg2wUyk7im2/fvGV/bxSY8XclEDlP8eZKoVQBZ3rcLG18fB677635T9TFd2bWN6nfVB30oxTaMFZuYCLKwfYXnzxv5ONLBb28acAvpZOYPtRVN1+wduX10n1XgJ5+pXXZ5T6YBxP1hYSXUvH5NI58AnEsByiBBT1+G2s3yPd3VbHJKpFCTg8cG/uL0aDnCl07Pk0+DbIETPZdWsbk1lVnhjbPTaeV4bWATDBmHaL054as/MEP/qClizQE60e5mProlGruOGSwqceLXRDeB9qfblFsRT0lEdpu3eu8+r6xLK5S3/9675Am8Hcc5q9MlHg3vJ6ReynxswvP2GL3WqRbW0UMkNDC8tT1wjKf2T5IEGYA/G/WwcHAO0Ng6fBdxu+xZKMgjDjfU41DkfnZ2sRZdwzBcnxruK7CbAitbFdAr8t6v8j/0vCX8gvJOLLySvPl1bfsbEyGoJQL5bf7554qlB2LUcuGgOqnXZmZI26XymECb3cOn6vr/dgI+PqSl4//HgnP6gIR+SER+YKI/DXa970i8ndF5P+cv2966Gmc/mJt8yeJSpcedeOlfQX8ozKWjLQ8nuCPl3ioEY38eG7Z9uMLcnJl0s2+nIlYWN5VBsv5C5l9ISDyuRUM6+02+2bMXW4WwNf1wvK+ToNbdhU2LDDFgiT7MhrPfdiQ/Tygx4tEAHdhvOUgXAq1WX62LwnZTL/TL6SZhKBZ7kNJNSyA3+Qw9yQpML6JiDAmhfisoj4H6OxKfTAD8IhD8MMA/pXN/u9T1a+dvz/3wH1GMOln6aVEmcxPb0kxu3P3Xeky8nYBv58Zlz1D7kBP2pMa2tgAXCTI1y5Aj3AldseZ/WVZ4Tis6/D75l+0RRSjskQhDFaUi4bc7+Gze/kkls+Dc1IZunEvj7I0p0a7lozA+lUcIMn9JQ2a13X3i3kC92wfRsPjTbS/k/3XPyqPDfMH67Mysjx/zgjQjEepLcX7w6cxuw6PfDTzJ0TkI5tDD/DQvftSYtl/UZ3zm63dKLGuPnTXDUOqdDkj12gTQOivOb9B9BcaWQAk4TpNubDwh9+Zc+s9GwB3GXZhfyzkvt1Ryz0jqALjw5cyr1tZxpl/DsGNLtNObTId1b8Hg59iIBbJGdKLNry+GAEqkp3kN853WvUNsv4MtB1LqtcdUxFchbQA+G5glqdrOUppn5pEjTgksLNL1QZOmn1hmeL+SHisCXAf/j0R+RkR+a9F5J98LxcuFq6wRQZ9Wbqff+3jd2IZz08FNFX9jdQ3oFBj21Xbvcn7VfRTxu9Y9sIApOc8oO25gWu3P60/czuv5FcMY7I+MX5hnEvpycY3UT5J/eiiSpNpLBNrWGu+ld3Mb+7KYyRdMjvPEWjgzszvIDIjwmx9gXrllXtKoAgUVkY134aBrXX7jlpJRuw6vF/g/5cAfpOqfhTA5wF83yMXeZ76ti6J4+mMbRlvPjHY8xj98cPSor+oQABJHrPUN8av3UX1l1OVHrLLbjcgDxgAOlQjvbEH+Wl33QF+5sYQuHFUY80N+DeMb2WX+ut7BRFSwYtEutMnrwzo/Jqtjdqj+fGd6QvbM/h1+vYwH9+WpEJY+qPUmwp63774JXtDh8zn1yn99er8nQpZGkxZ4tdjwfqPSJL31aqvqr9Emz8I4H+6d/4f+cOf9fWPffwT+Oc/9omV6Q38msFtcpIrXVS+2I7CjTS7ZXZmL0AToW3vQUVcFOtbzCcgV+rl/fHsel2Yi3iyqobL4edpKsulWF31m2S3/fyJqvk03d4hjm97VHY9LSvgvZE1VWhQLwktC5M/9iO146ot5z/3agULk3x345bZNi6YYEeko+bYFev7iQ5sA6ygd0s7FZsIzo2iMZ9mNGyuRk/RAZmNknOa87/yV/4S/vef/tx1vCg8CvxU70Xkw6r6+bn5bwD46/cu/n1/4Nvnms4uOa4YSo1yBHRdK1eavZR8+zQWfFr7XQoEkaHegOfryA4kqinI0E84TyW52U+XXrkOoyrR0Vk5M4rtPKwV2xM5M1ZG7RcDvKcrX5F0ynwmz40XI/D0wgDkMRQLayXTRnnklTiz/e7HZeWtMp6nDKSdlCnpTIDMBmDL+H7CmtvbwMalsjc6FIKT1L6nb/PFn6RsiuqZMR//pqvztb/14/gXPv5Jj8p/8QOfvozms8AXkR8B8I0Afr2I/B0A3wvgXxSRj2J8UeHnAfy7z+dIZIy6NeWGIxqAwxVrGoCTWF/JOOTBIrmirYkBVZbIWTcIBvRF6u9k+sre/ggJg+GVe6fzPabO9ajftTHQp/RcJm6+my9CrwOrH6tB5/09z+az+EMYXAbP/6r8XFM4Ykr/KtAvDQDSuptQXq9ZywllwKOCmgwAkA0C1yf14tgHB71tZ5neFUCfcZ3PP82gcT3jX0MyBLYvAX9Zfz480qr/uza7f/ihu9s9yrpLqFk5nOHLYJDFCLhxiD7jlWVCvtVnW266lQUC4M4iks5NNSkdx1rJKraLgWDtYHFjSEqKcX295BmpbzeYw3VV7M77peWOVfCQwKvbZSx/UhmczPTWwJo+jWX3rHEMlXVX6lcD4OWGAL8UwHN+ex5tyIAMgFcVW2Gmn3l+BfadKQ3MmzKL/GxQdOmwrwi4SydkAGpW2a9RGslyJOBfqd1NeH1Tb+2kkFeeC5Zh+c+Mz9KqJr2wtlcYMqHO8OBtLoRsBLaE7/aioN9kWpxwnS2F1qsuWMMG1PbNgsn4upiX2NQEAGO+bFwX43tu5D6Vy1BxwJbxxfKvMvjzfr0basrHKEug5vCikNyXZ0aGpzlIA5QfuwJgw5BDqAIColuWjt6tGsqc2CiAn5leR1E23i4//wxZ+Pix/nx4DbPsZtnkrcKLP7nz7ekzTGQAuPDsxpXpE4gL45hFYBlZ91U8l9uv22TBhUx5TGZRLrqqSO85zGpPPn7kRoZEqN3C9mpq6jGJX0dc+gtXhB0HPyxfHgV99fWRyunajOZM1LRd3BE2ghRpV0O2Xe9154GmInRIWkAAlT7irwIRBVqDnCfOCnq7SzUE9GO2/zWR+l/MsJOU0Z13wexdl5F67utTX6xLOi+3TQbIWukMpMHlmf35FxXOzgXStDSSq6GznKsDkhHMhu8P4XGTmabou81KgP8CoWjtHsz8MS4/53maZGJp2KOW/aS8lOLEWcRG9VG5j1BpBn5LKqkCyB2iZiVigGe1U1UB1Svdob6uW77y/RCyXwDo/GQYZKiy09OAzAViby6O145r45/e+fdIeBHg904sU1meu+yUWIYkv1dEY6Pqz3NyHVs0QWR64aOVCrWZagvZukoqlDgeD2Rfs7oOYWiuymRbWO/VGFAcOf3jXlXqz+1OvjiK+zXdrrX/uFMbTHeVlkHPjxN6JhnWoqDMSIrnJyVGIo+nt+/3dEUVd4dltbVrqm3oMBgOSIpnUv/ABC7Kj7qe1dxSpfq5uqjneVJcqTJNq9W1oyPX96j3HYpbMqLTPKP3A12PsewH+m0sj/4GTa/NVj91+7C/fsEiISfz10nSoIVFaofMraBud7qNruSlVzeqgOM5Vb4SK814mCGx0+4BWmstvBuE/t67QEJSuH7MmigquqkvdrtKmWjsq0N0fcTk1tXKYM/5SflajIDB3EFjhiDB3fIdocrU7N2UyfM14KpAZtI9H1z6Y61nSvU2r3P97P7V3HN+LTekSUoghnGOcfbJcOiZQe/A7+h6w3HrOLTj6B03u+Z2or9ZwOd1HZNtOMPvffntz9knS8r0/T2qOAAyu9OnlmKq4vq+NwFeMuCjkmYBkCo1XcOydrVOFw4J+cUp4+h68/H4+nr3Kv8FiFnAuDymPn22om/KYZ37gMSmluiDwRvGMcC+NwI7tg8Di5TPoa8swQqjf11yKkfO5b/ngxkCMoi8dHezNIamhtDxKe1x1yihKBFA9XDAH24ATnQ9MsvbMdxwc2V8BtPfBtsfx1lrwja8jNRnxldmExqRpzXzqt+vyG+IzSo2LXtli6hjxV902d/yGPDGPn+Qi2+jVtKRHge3P5YrNisBk5xw9zsq2wgmuzEXpWpGlXXmDnHBti/8EFJDzPasPLyCsw+8Z39neGf83LCXZDE48yI/BJyXG2NajMAV28PvZuphHHNfn0A/NT7p/8iL7KbvAL/mSYA/Ro0u9XV+R8+kvmd3EXUD8Edi/EPbULnE8sb+Hfb8c8r8jn4LI3C+uYy/yUiWlMlPooYjZ5ri5yMarx4Cfd1XZD33G6OscyWFVTYg5LzvJ9anqs7t67XCJZVPBsDybtRbSVeldFsQWibBILkgYP5sNETFLz4g4ZOj1DKpyosM8kL1HLeqpmbcMuOz0U3wdrbnVv6sCMhQEOjdJbSPhFAmRT7MfJmuyg786xwFPIZhApD8+95PMiaztGn7cMAfODyfG1oF/nzezcvmQL8Nt+Jwxj/frE9osV/lLLLx5bVUrK4b8JN/ZRWWQQhcgH7D7nkCxwsjQAbALQqC6bniOVv5eWwQcAE+zevJAOhkcklqwJ49CEzHSL1IPXg1XUvGQLd/VxB7xaYeF96/NOwRaCI6iedT/K58+8UwFLbPfG/nmNEXQGW+Ei8z6UL5zeD3zE/xDrl/xwWi9+CzzB/+fZ8+flYR1bAcA+Ta0bUNQ9DHUp3jme3H/j7bEo7b4c/sx4HzTZL6Lm80V5SQ+HmEGE9umAaSbDJ/3nkuWS9Whp8TaLb4NPGlYdgYAGdyqqWyLEEVVlz6F07eZFAYAPubVBIZBwayiE+7SX8R2DCkM/tXJYBSIZ+RsqzOcqU3Y1zlbORbMsg1jx3sUYyunEBsLlQODPZsBsaxSQo2UIYzlWtOAuZcWplYvvAEI/GJMKV8WaeN867PlK9ZSZjU79rR+pD4TVu07OOW8KI6jIAxfQD+wHE736xW/fOM0USWIemLI+cuwyhD3boGowBw9nV29tc6y5dGjmjE48a8RrPp8vvfweRYlkgVNAyBL2HnjCXjzYIRcFS+LOuTG6A0O+48156hqmGIHPYO/3m2LkDniITtDGOjilLxVyYEHSu39BSzm3Pvt4I/lJVbjJSLbAoo11n+63SG0ue/LB818t8LI4PcBzGRyrwa0LR7mzF9iHML+jHGThTo8x19W5cOdAFaF1IQY//ZBXICimOW7zH/9rGub9DIvd4J+PTZK/8gxvb74gZ+srYO+gx+7pOPWVvWTwv554iJ8ZurgXsstFsaI6Ew1a6SjpBZ/GqZfU07tn6Dr/ipNhpsAX/Jr00cGAQOegN4kqbMjkiGQvOtPQRE76iqK/BzGsRlQLk/yftkeE3pkMSprpbqYtzqbLbee8GuTlKitGTpT+uD2bNS7cmwCsVB0DuANtSA9BO9C6SfOE+ByInTZ9Eww96g2tBUofoGTa/difH9hQ8COX937KyWlKR++PXzZlU+TsD7F0aO/UcHfMYXWt99SGOR+6mi1ko341K+kWeBiDX2bRg+zteyPfWDYvZiSDrCyzAC5eZM/lr2lWUYgmIAShkoXUfWOH4JsMTyyRDMPXZJusVO7kfeh7FlFTYmvhDQuwobqZ/TmBvveI6Iy56mxPZru0e919JYCEXvo/6oDJY3xaId6P2EdJlLQLrg7ABOTKM1AK9HQ0ND0/5mAf9kxu+apL1/X7yfwfYnW1NuNNJct3SuTFDazKwh7zeApw8PrOxD4CfmSaxkoHewV9Z3zrkE3Zb5N0Ac+0POO48PCeAZMc7Zyf2NlSnP2IKefPXk/yawkwJLxoKfW0BbjGmW+nG+HXMxv8h9SYD3bHelYKmfRmB+8FOjZFK2XDXe7aQ8A52lf8j63CMSvR/xHP64iJq8r4wvA+iD8WNdOiDnyPQogwF4be3NAj4zfoD7zJ8UJvCfu8zlQvG7DWYTAX39hsB+ZNC3BfS03daKWRVA7LNnE+u7YQCBzBh4rlPcE+MQSIut8NtJaYVO3+cjsLMRMF+8XLjcnJnPIsZ+PVLFRSiAcn1+2igbtUEWztzXUp+NrjN6kfsuIAjgbCjGpozXkh3trDZmfrlRK1KfWL4agEd8+04GgO+x6w7Uqc78XX0F0EOcaBdoF5xdgH4OpheLvsJ6Apo2tDa/0ZcU53V4cR9/9znh0e2x8+8r6As8HIAh9cO/N/AfK/NLGIHdWP1di35WAcH4URmnVFXMT1PP+FFBJMwVPI5dBLS5TBfTbYc1APa+fWb8aCsoxoNVBrN52q6gL34xx9cDx4GASky/KIDK/gZ6MVVFUCe1xSxv58YT7TsKa2ZrWiEw0jsKdZjyPaBnsLNSjT74fbdnj7zso7p0lWEAmkD6XLdXeqfU1ynrZS67yjAAbxbwowIm0KeW/XPx8a37KHXfWYbNey9+ObH6YaA/Kuhziz7L/PRDvneS82QAsiEADY2PChet4naMlpesGcHV/VKuAXI2Anp1E4rDbp27SNkIMPgxmTJYk455IAkOBm+W+iv727VUBn67SLy7WmmbpL8lV7BAP8QON1rSKMVpAOo4EiakKyMQoI8Xm65mxk0GdILel4Ih8RsG8M+ZZZP1VccLPk0FvY863d+0b+c9PcWggif74CAtn57ie+PLxwe5S8RuoiGxhwymr4dKQ2uHM/0A/+FGwLZZFXCrPtVUAj8lRtJG2jcalTyCqP7uHtJ87N45EfxMjeiwTZjQH+sXxiQBoQDWfM2Imy2v1yPtc6/U+4YrFuXFRrd86307Aw/CvbrIC0V8xEwBm0g3A45BXD+8WnucqO0pjTNhMuJEC7UztQPtUDR3KULJmbG0b9oHQR2xfZv1dZ7D6+yael7Nrw09El4c+AH6nkD/ZPvtx419vV/AYSS0lYE5rTUcxPgG9vFbG/z4+sr2wTQbwKf14kNeAp62LzA+Ku6FIVhY30bwTUNIsNi5Fmw8F/DXRzl6OO7TpOgO/FjAb/fJyozaY7x7tba1ZAXGbsJVGGMe1o9+1sFf/BGQToA/ZzuTK9Dy9dvketbJRsxDcdWpaIfiGN/FDkIo2ZxAnwwA7avHDu6BQnFNL7MnhRcawEOM//QM6J+4sS/68j04uwbrR0NdC0vbDhzE9OHvH76UyvitVjJgZTfEuvA6sf5w9LEr6HR9agp7hOsjsFmQss8fv7lm1bzMnrIAXS12rAC05g1J7IXpDa/s1wfopVWje93mkmV/rITCCcW1sLwZABt+XN+bJ7ZPPUtXbU+zfz9lqMdfB+HUEmV3ZVafe8AX2hYek3KI38DaRMQy+k0C/tMTNe6dfQC9gt62ydpyppucBKzV2hI8QU8y8ShANwNwFLZv7YiK1mrFA1U6+HJle9r2Wl+Z/wGZb5NkToMRo83uMJwdnUCvBmDn1y7rmtk+gA43DKEShIxFBjzHk5k+UpklvTQrNyq7tD5fnyaD4RV9lxdqz56p1w347WWa+q4I9Sql9iZvd7L6WF4Qo2dYumW6nAr7Wk3U04i/uDHcgt5HmjbIEe1WsSQlWqrfo+G1ML7792YAnPFJYnX28dUTlVpwwYzQkt8+gD4Zn6X+leyvklL2lXrZdmPA526gJ0B8hpp267VJuAzFHiR5775uUO+2YWtzSwO7znv6AZXYLktZDEAESZUymLw520swfiuGgc4faoHKvj6LLKDl8dITYYNqlolfrI6de5avA82opd8a6CJOMqbIgqChhfQvUjz751fAt/yR6K2S2M5lqFW8PRtea+Oes/5ctz796t/33iewMbJTqNpRA8cAbwDbwL/r1+cBPSY1UXx8s8qYz4qn8jZb3R3od2x/TwHE8fD1N8GJLfw6+nxGYn9n801zwXab2D4fJOm/LJ+rdXYOjaGoP/fz194Wl7FFzg6BZZmBEFDQyfqbPnTvLYrPsLk/z2yfVMDOx+cuZriBatKApuOtSdH0NqgbPAO9g301AvDrkFxRada2YIY9xmw8SiIv+pKOqq6gJ9l/smUtA3mkjbeWVKx6bxqKmPFbtIIex1Ea+I5kBNZ5+IL13XfysMA+pKgd8f68APi2ey1qqW9fQX1tsou42F5hya/5PtuGPCXDMGX82tUXqazblPoUn7WRrzbY8QCqlsqQW/yX0ZWkLyA0ktEzINKduyBp1pw6RoTfHbFRpDSwLBr3TrCfX0fjMRmhIb5iO/39IBuheieXjXfxlWC4UoglogETuWvwoqIt4UWlvirCepKF7bNhL736WXwoGwNvxR/99jwe/8BxNByH+fdtMv7hxqAO4rF+fQa/GZXplIWax/M87Sct7To8bpx8eCFIT51u79hDl2k2/Pp4EB8P5otptvJgoOje03ScR0e43w+q2NwIMGmO7AdYxXgrPqcruWVS8r8osWIEFoOclFcOgX/NwLho6Ns28tnS+uR77o9naW11EDPNSWHNP7vh4+2Y9XHXgm8NeAR0e9askutLP5TOR8JrAP6Zf2RZedDE7lNM3ECSpNIi5c0ADJ/eDUGV+gz6WRmzdQ3wJzm7a0WpGNzI/QR+zEZKBrnYTDEE/ufEm1jPtUXBniAXoA/ExoAcdbYPkOe8564rBzsphZxuOxjbmfXD1crDqLMh2CmxrK5yWVj7BhdJpAv+Wmx8l8Gk+/6joDaRZszuHKPsOLk8/qOlOE3p793Kox4ejZc78M+BOKnqCT0PMd+fG7LYfiQ88u28rwTwxwB8CONbeT+oqj8gIr8OwJ8E8BGM7+d9i6r+8u4eIfVBvlRm/n6G75S/xZ7pU4yFK/gr6FNLfiwz+Inp02w8LVh+Vfm7jXs5mONvMthWKsM7+F25PhDI6hTJuwM9N0kF1+8Zf9lm9HMa3Q3IgJ9UGK4TN2xJkb3LfAnRFsDl7ve/oHwVYvydn19H6G1H4a0j9saHL3PueZyKYYpGSXESCiIiYtr01x+z6+66KaXMGUCvDn8xGf8JwB9Q1Z8RkX8CwP8hIn8ewL8D4C+o6qdF5DsBfDeA79rdoDJ+n0zfC/jXFxmw+J1m8aSy/QT34aOfYvDOQWDPXXpRubIMhZta8QeWRD2K+3SBcfEE2A78oA9gmCF4qMmGAGdS/wL0Jtcz4xefNUnHrAB8T1IB7NhHWodnUqY6X8pulfmxntne2I8I0FM/nqppVq0Rz9yll/rzCeT7LwH3mIWIpPRw06K9ged1iAa80Vt03A4H/nEcuN1uZAiCtI4j+/xRdbjVfjw/fzK+p+1HwiMfzfw8gM/P9X8oIj8L4CsBfDOAb5in/VEAfxGXwN8xfpb7/bTJBZEaK5JPNXIhfD/y7xnQx5Eb98wQ1H78/KGNwvhFwq30vwt7kAoMiBMQ7r/D1+0Ltzr3rX7+o/BXrh9b0NdmP4J27CWDu8jbyvhmoCO1vhTE3HcD/I0+bLJrb8lu2FUDX1VSWKOTXMXclacrcIqPz4N0dnMLerhjzGxU6O124LjdcDuOsX7cfF+4AGvr/rh/tN0DmJ/ERo5vScMj4T35+CLyVQA+CuCnAHxIVb+AkcGfF5GvuLqO+/F5cM5JrannBP64H2jdJCvc0qf+4OTjUz/9Ed15Zggy07diQAz00YgCXu5zZCxIacd+3SzpmJg8OKjk9gAAIABJREFUnowP8tWTCtg8dlPfOQqk9rEFvWZQMHVfsTzyac74q+Sn9IZfk8rN3Kul/Irsz4N91sa9Cn43ejOyDP7lRZnLl3CYQXu8eGMDd9g8imB86opIaLqZzuJtMPztdsNxi/Xxs16n7OObIQhtQSpj/h3TcY0PakSD5PnFB/6U+X8KwO+bzF+r3yUhpe68xPZ5Jp5s265ueNWiT8w/My8xvrcDHLlybSfbnM+Bi/1sDC7CXf9KLfbjr0lFBst4fx3Jz6daFnpC8j13QuQ50AdrawCapWxi+cJ0BHi3Gx6fYuyEGJrbZqSCnUHPDa9hAIa0rjK/gh8OendfSD2uH2gltk9ddtkAcAMaayMRGd13wkNqG47j5nXw1atXDvZXr24J/JntsxFgqPvaND69Hz7GoM0BSK23RLL3wkPAF5EbBuj/W1X90bn7CyLyIVX9goh8GMA/uLr+z/6Pf9wL5Tf+pt+Cr/qNv6VIJ0RFRxSokyKAeNXWMogaSW4t+VA8cOdohzeWeIOeVzYE67uaiGfvQR7cqrRustbBVPPw3rYBHUGSvLSnmq0o1fyOydUChDBO3kxVmVAVPLHpOn12HrIacqDECclUZam+GNpGZcCDqGzILgroJ/vvDC2xhq+6UcsGLnXt8TEHGGVxar9Yqwa3Kd1u7M/P36sbXjn4pyF4RcBPdTsYPwyNpjLrfczK21pH6yd6b/gbP/NX8df/2v91VRlSeJTx/xsAf0NVv5/2/RkAvwfApwD8bgA/urkOAPBN/+q/CWDkoQ3gyf4SF6BZdlAjjiQ//qDuumQASsYla8oyXwRNQGyPkKIghY87BE+kVkbObEHwXIh6zMwfdVug3mC3evy6GJZ4fGa/YHiq/MUAxPvkV4ZhMlA1ALKmOPi+gj731+9G67ECs2tDg3GmzYKoL0YR4E3xuEJZfpmIRpZZ3lBq+GMcMoteZ29EqZPu298m6F/d8Or2KozAq1drXW0MfGP5DH3omF67nQ2tBdP/c1/7W/E1H/2o5/2f+hM/clnfHunOewfAvw3g/xaRvzrz9HswAP/ficjvBfALAL7l6h4+EQeQuh1CVhr4ScaJBPCF/CcHcwF/aTn1DE2NfxIScvMJ5gz+gJIZghwS8n1fYF7Luc+HBPTC/KCnGdCdTbdxC9nrMh/MfHdAndi9p/JaWH8+Qx14ESGP14bh99tXk6FYeQT779tP1lwIfXPN9hX0DH6+dZQJOxmj3nCvUjD9zZk9wD4Yf0j+Ffi8jDYFMrYAVI3tB+jbeaK15sbgkfBIq/5PAriapf93PPIQa3DQua5lgM54TrBr6get45pbSxmcAR8+kst9kvgh9bO8h+zBjy3gLSX1iCImr9iA/rppgBriHgC/r1eWL5U/uRya2a+w9e63l/jUpcVLUznEhuHXRypXib8DfZvszpLfrp8ZYI2gu0ZTBIsb8lepv//1dB4ZtgX9XFfH0cH43E8/62YxAK/8N5jfB/MQ4Jnxk7oiI9D6nGOvn7E8G872Bn1JJzE+D55gqchgcpbnRjxj/crseUDEvn8099tHn2tIfKujggL6ukxhxll1fsZKUx3JrL8Gvt8A9KzOCejx4QxvB5hP3r7ax880ti8VeCfVn/u5IaizHhP4bTIQnyIsuWyRu1nq0+u3yddfFRm4fFILgidtMbn+e4btfQRcCGqUW0eZ+Z+oNz4snAjJJb4vXwXbT/DzEPMK/tzekJcB+Mn8ZywfCS8+2ebuG/fJoCIacdaWeymt9pbJbWF9awe4GijSGPCpgpJfT2C/9KGd5IkG/OB1kIBv7JGQmNfMX8C/fdam2i5SNjP347/124VqyApUUJ6Jg9XHSFRffxlEVc9BAn99RnJ8LpDvyn1h+kpCWNZz9grVj5lCEbRD0ojRGw3WCaAb85sBeJUY/ijgvw98BnwG/yPh5SfbtO+x8bfWoliK7KZunyTjp4W85RZUzrwE+qPF5A7LgB1kVicJ51WXei6jyoV/6a/SuIx+JFf2J62Az0AXYAzi8ApZGJ5WQ0llmZ8ZG4iJIaPfep00sm/kP3Nqxf404cVfX1rzlxF6ZcotMxpgQqilkfPSweu+/T0Dx1KfDRnXy1w+cFcw4slvfR7eqDda8/dSf/xyw3RupF5ArwqdX84dwDfA2+vMJ+S841NSePGv5QJIDTbSxqQFAKivveX1o1HmmYQ6fBDE7cZGwAbyjAz02Xka/DXH+KnFJrO7550WoM8Tr9K5A7OlvQqCzdXr77lj9byIp8x181vZACDJ/M3c7zQ1dB61tvmOIf3W9BEzk78eRiCfafXCfwjDDEhKqXXnpflM2VWMm/i60r4Ls5tWxeWgONhT+1MCfrD2bn37hh61OcXU8PRTzPcOaIkWed64ARA4dJ+qXXjRr+WOjbFI1r8N6IdPTjOLTiv6KvlMGfS348CNJb614tsIMWsgdCXBzK5UyJvIy3Y1p2c77/UGkLvT0vk7ED8C+l2BmwFw3ovzZmUx8GPzvTceo87bPvhlkcgkUzDeUpvkGP754l5RWUjkf/A8BcbxkomS3k3YZXKlnuvgEZpgJ7WyHexFwCfCWt68s7EjR3kZiWfXIaXLvUrbKGpDg0J1gr+tBHsvvAjwkwSdS8vM1hrQO7QFyKtsb0cd6njL3SU2HDLJ/OF3uaynzGVJHzLfgFJ91Pea1vlH0w5fk7R+Bdh77F/PWZ9zeS92DyagAWLvIu2VR6+ZG+Czy64NZBAZH/eUQHBqr7HKTczuEh5hKNwIENt7IAMxfCFOOxuAuMGmKPyeV1DJ7T4b0DdulJT9+/UM/qP5OHyfaozczuhtYlUB9ig9v1QBbQLVhtYm+C/ScRVehvGXPSMlZtVaG/5MGgDBDXY3lvcHtZQeifXdX7IuvzldV3QNhnLj52eFSKxxwfZysbxMuZb1S+nwKNivzkHZV0A/19k/D+kefjyzub/JdrHN/nQCvSWTwG/7mO1XkG/ClQFIjSGc9ig7dnz2N949ihg/ZAu1SxTw0zj96EWyF8N2k7zaNyAq40tyR2sFjC+CyfgybhMADe+B6D28OONb4BZakVFxvDvkNsc6G6CtkcR/h/v6yc8vLaPtCMuZJD6B32X+iErE7wGf3lE85b6AO5iu2HiXF+yH53NHS/8e9NwLcDeOxa8PAxBfe6lMrzup75NMIk8/ZQZkliNPirUDuqsAbH6kylaw5xUDwd4A8ASh+T73ZX++xv9N0Me8dzYKdOyLMfbUyEdsn7uUN6Bvqzod9VOsms7xUUNVabOWsS28ng0v7+PDEiFeKTABwxMVWGu9DXt8tUh98vFvoQzSiL35em122yS2I4Z3Ktl96PMtko73ZTYE00bMc+8Zh8d+glqRS6RI57qv7379roHvjtTXPj7fzExP6ylqDYUpGfwZ5MyqFhLGuWwSLgfojdWVCmGdFbiuX4SwQK4UIw0M+gx8H2NSfftto94F6Ou8j5YSiSm9zAA0d2Ma2psK/CVM0HPZNGlZ6vOwx1fh0ye5/6oAn6S+ZzCxhz+TnpuxOqtQsgpZm9/lf7dwzy3vXZd/+wabcBt29mZ7r8T68y2zZAC4ES9mdQkFwD495joc+OOJZlwirwLgFfxTETiwkMrKjTTfzFakptV0sMxY1IuI7bdFIMglO1sdhOM+/ETufrR1l/Ys97llvxgBqbKfQc9tUaW2DQOgUwgMUmvSgfbYoB0OL96dZwly2Ud+1H6sM7/dVOT9Ea82VsBbhm6Z1dCitQKN/TobhxhQrkzUuCTuvZ5tS53/rbAy6/NHMDLQV8mff7Hvis/sScrXaL5+GYjjo/M6dHlRx/r44Q18ln/c972EytBW/qjVul60SxkZYBEvp2vNoynpyrt1zWEUQyPIbM9TguVXiXlgWF73kYnLC2HpQRQRy8hwGw0/2bAGSYnNCTCN0SPh5X18k/jm5/3/7X1brC7Jdda3qs/EIlgkjmLHYIsMxIIQyWLCxcRxXiIBsnhIIFIkkggFhFAeohDJDwTBg7k9hAeMAiIRykUykYgtHIGDZOVGiJCdmcHYM/H4gu2E2IntjPEDEEWRztm7a/FQtdb6VlX1v/8zzvx7pLNrpk9f/t7d1VX1re9bq6qryco56Ieg3ijr2Q24N3T7OfhdqlEh8uy0til+JG1EcxL3pR3a0r15tcZhf0jgdwkaiOC+7hH81r02nj+yNRbn3VD4PS/5mjm4d9CnXzW9VwE3DvAl2mFme6/u0PVM5n5sef5BSmB5iOT5yiv6PeeA/cMAKvn5BLJ4B8S6j2dj4O+bsIznWEbKieTgK+XfCGRZzqNCuiFdCPh5mwszBi3QqLt7ZQL6Y4npM/iniGlSEiMkNa2PsBMsDwe4z+lvTGOAT6/kBtyD9ZllsQT/Iei96mM7982fWfAT+NkIHPj4C3+fQZ+WdEuynnDhTBWfc3nakRrZflQCxxXoJeTqZAiG6uKvw06F3HYDUAjwweY8gYi/9k3ddTZqdO5Wns1cC45KAj5oaw361siLtNlwz0kX8vEjs/aoYQHDaqa37rbiAH/sMWb8OaiXfSW7XpsVZYQhwKBa5dDyORgMgc+Fb75WA23E8rPcz8zcAG+Am8EfAEc2AgcR/fPLnZh4AH8afGPgH1g+n6PrrPgSeUvkvGT3m/b5GEt8/u1UOWRUsz1KRsEOCe17fpmhiai83ca8ejnYlxVAfCqMgnduDIOpnQi6MkxQ12wEpthPd03O9fYvz/gIo8/+kkVC723B6Bnow8AdOs+NSBnW1JZy3G2QpYv2o91LUJhFNRYTkwCAsb/VkhjIM3ADbMfg90wcgX6KSimWb+ctC3/ITzJI/dg4eIe69dICOWD8eThSuHTZp595brV/syFYQV+HNQOm7y6NQPy93VsdlA78BPrFnIErX39k/BTIzNH79Byer/zvqoxXLtNN6Rb68RU2pVJ8KokDe2OAbzE8dxi4kws0b/v9ZWD6AUg6bPjkl47zLvWN5RX0Oa/QBsuGSC0t+fk61NkB6DVlTmEwW8m+xd1hhk7p+BTYe5iFwO+Tb9i1UhlIxmpiz/zsJzz7+cjhyXN5eNGPBoCW5S17XtXakbct6y0aJgydmD7iAJOPT+7DYX4PAb+udeGyPiNdvjuPiNMKw0FfcleevXgTrzMS2/f15sAPn2xsVECQtMmmtjoGDp8vgEt9CP/YZEH7cKUBdGT6uYkltrftQ3mPfI103g0F7ehEADPJdnXZGDK/xlo1s71aYK+B3yWzAt7quEzNx5+jWHHKSRdgtT0GCPp+jcys6tWqhrsfl+f0em1TnbV6hQiQPlx5wPge1Bt+S779oq8+FY9ljqL6/d9RudjziyvQRfkcpIsAn+s2utqGiGiJ0U3b0BeaXqe1CgCcORyeCnOYO6CELP0oAqMrLLUDkvTtckqyP5h9MAv9iPWzrgCs/legK8SOcp1TxuitN0V6Gy2N3NNoIAHILt9T11zMGMsfY1Dd+1JjvZxyi/0T6eVo+ggYlRePRY/FurrybMdFRma0Op67/+zO0K7Czmnw7rsf/c4uXX7GthV5CtkSf5uCdszozvKUD7T2dBSJP0lKrlppdZP4G9KFgC9pm7+Gui2WUnK3XPqskhfuaNsIlgqjUQJi+yFhKlw5KDQX3uDTM8QbQcfnrtopZh2IqQfmZqNMbYIKKlfoaJnsFhZaiEegjBsro80mE2/U8Rzy9nHS3ZfV3PJzdN/YlDJtswaBJDyortIwVZ5cc1WvOfhlctjBZsbgBaUDv+KQKdnCIirejNDwrGuQS+Q5/lkkPfHbiT+BtYMw9uemiwO/pEqPEU081DbAL5MBEG4MAPLjih+xfnY/J0lQHeJiR0WmrurjLEYlAVVkML1Kucn3iDayasi6bAMuZsTUTH8ksUrXkLI938HYdSHdZ7BnWT8agHad9Rc+xP8VIL1hloJbC9aP7jCLkBOoxLAmdJdhPRb7Qd6WuD46v5epwOwa1TWwyCAGxh/3s9F5SHLO9+ZW6y5r3j8nXVzqp77NxPpbeq1xG6bcikE5oAJlpkPIZQLHGFgcAbiAsSHK/8YAb/2lblj8L4GxcoIkNJUBN9rxt5RN3h2YXoZ1mKTBZweB3friVQ/AvqPaogH+PBPP3LCEgJUlPmiMhoGewE4S312BBet7uY3ruZimnK0251NkUQH2O0srAjoY76xQeomwAbDjSbqMLfFk7uc02YFOckMA81S6FalvfZpJ4m+z3J9Bn2WVFSEXUoKsy6C5mBmYmTFmhvZfIqJCN5vluV/bM8YmYuza4qZgBwcV470SzPRjFjWedfLLyZcnoCewD4YgPhOd4wPeU+yNWKPBg8lwZPzBbet928Wj5PNSqJC4jtvt+r0rotJXrT794XChlXEYGN56c0L1W89EGILxmUcD4LGBlLidzb/GkOT5TxqfZaXHLt456VakfnweWXxyAvvIZWN9MwpxXiiFaGBHqQEkWDBA0fODDPqo5jhH8z/5eah4feQd/Rr/YqpTEftYpjWiRbUvni0z/JDnxPKN6cGgH9+2O5L3CfDUl08+vgFdWIYA9EwYwJsDe94lluZALNEufAGSTz8opodLYyWccaWFIRmr2cA+yv4csc/HQyqsbro2Avn3Yd/cO3LzzkkXlfpjUIej+TECLzN++PkmC+G+8cSWVG7MiuQV4Xi4a8hz9QvYTzqcOf1pf0BE14ozB9zqpzJBMyDcsPM4Q8pX6qkIoM+aL0ft2V+PIbftO2su7/t+TT7/ns5Pff7orO9ulX0Cuz/jAHoeux7j13Mb8G4ykv5ex0c4UPe5WlEvx6oe/TFZ20OchZrxblwCObfAxOwsR90AHCL9MB2xvf3G8SsD/dDYT6aLAN/eGBLB1O/ZQL0R6+fhjzEmepT6jPDYHSnRovqpU0/5VPOPuo/sv03U7+cfsfmg76eU/q6f67bKug0xBgWD2XPAkkjJgzy0diZQGn4b02elKL9qvILrhmM9eGdy2zzuglRPCdySt7Mfb4bcTCNf/7Ao43c9UeyhuimfobIclAZmAq26xMp1sUozjUj6bf6FDYGkX92kJKnfHlSQvqns7TY1/Zcu8GVg9W3h1wf4U2OKYvGKDsOnuQypC8+3eslE110OiiRLaoGSFDkN4yF9I79aOyQnjWCPdmvxC6R5Okcj5s8yK0/eTwqFfHz29eMLRvlLsKPMTy4AXceXhWo2prNofsj1xWuqyWXLhgNeuzclHdaLopfYcJ4g8E/+ec+LG/fuV6ld7CbVh5UBmHKF/Iz2Lxs+Oi7xGviJJ73xrqt045h+EXmtiPySiHxERJ4Tke/rx98qIp8RkQ/25c2HN1mCmhh+mJ4oGQGJZe7Ks0TsbA20HzBQxJtoyOw+gv6A6Vzyji+vLF5m4WuZLHc/zDKa8knPAKT8A4vnTX9G8s6BOjyHfeaZ+vDrAux5ei1N4NfBEHrjdRad3bjUbUv+fP5AZjR7OQv1YyM/DX7pG7yd8uysT+eRfx4KZ86cCuaXe7CoNXMT8oHJDIzsv15jcc7R/nE6h/GvAbxFVZ8VkZcD+ICI/EL/7W2q+rabLnAo9QnoG8n93MfLfl9YZi+uxJIEePaBLAgy7DPoDUDcwJ3l3WVoaxsp5nXJrI3IjjeA9DqvnUB/nP6ejJis7fl8LPKfJD4bpnGSjWrfh8+LnTNJfStriXXUB1Jgzn18Cs7GiyuD3LeiMOwhtl9oGjE2xR8gg1GIvKjVF7G/1aFndsE+Z5kj//vjbXafeCi4iQ57Q/SLTed8NPN5AM/37d8VkY8BeA09yo2JgZ9l/ZaMgIF/fM1xeruJru0gMEluheIBMT5OPrwDzCQxpkZuFZ8MgMtdAyyZeYkNqxq2DdGgF0ZgktE67g7Pm8u4P50/Jybw14nxZ2lPjD+A341kyhf7zRzUW49l58Dd4Ug9xPYpGbtQ37ko2ZBY2fsSOyz1QYBr11Ifr6/LgUtDHehhbqc1Oa1pO/+NLrZXvx+dc5wearIuEXkcwBMAnu6HvldEnhWRHxORLzu8yUrmc7/9lgfvjMG9PIa7PWQupAH04EinsfUA5MSMA0uO8l4jINYkc/eb+29VsyTGsHbZ7wo/IMr5D0RrOtye+MDKKi8Z8PyM1d+zX3fn+bj9wb8fn2fOE/vqkiR9BPZoW8b6HBn4qBUNBbI4npgeDO7RwBjJkpFxgxDr03JbctEvczU/TK5H3pvvwXGP+f7jVR8unR3c6zL/XQC+vzP/DwP4J6qqIvLPALwNwN9e/e173vMO3/66r3sCr3/9n53l/palfppCq8yNxBKDPR9hxicWt3MG/1r9WAZMkvn8N8bwA9PnnPSyawWI3DzE8+XBP7qMP6JaF6SdQk1NddjnnzQCd7Qd4DYjoAn009eM6RuHnFyS98VlfhqJR3VJKi73BhgQs/TnknRRxcU/0P2RApjSIKddZYhpsYicp7VkYB5K+em3I2DnMhjXcW60otQOcvMFFHjqySfx9NO/Mtxnnc4CvojcQwP9T6rquwFAVb9Ap/wogP989Pff8i3f5Zl57LF7PbiTfXmTiPYSB+myQfoNDzSQ4wRwkurspwM5MBczxoYBMKb2a5LGFpH07Q1fC1r3fTGL3+9WKfsizRUp7Y/WPls0elYJR5WuadG038CM5MvvSfazj1+hFTELj5dWACvJ8vTiTZnk/dhvb0bB2daKb6pW/xRpKvdFbU/bQ/NZuiPJ+FC+qpox6n6++fZh16dqim7UKKNoQ7PRNNDb9modhifaUfqdR471rqE3vvEb8Q3f8I1+l3/9r/7lIsMtncv4PwHgo6r6Q551kVd3/x8Avg3Ah4/+2Hx8gIM80VjmzyQLBLEfoEduJb0yomzDIjr0GegM4gH4Y9/1kulVo/6lN02xcu8ZKR3I9j5LGafMoEqsAhRNVZpn/smNyQFuzx7ZGpiejABNpZUCebtiHwN7VVErnPXZeMQDRMPMALLuuuIGPAX2zNjT37iykQFRqY2PqIn92SDk4kW/dmbUVVyC9ou2AJqDv/3msY2QYv1fa2dRCWwIQPUlkaUoQxz597xOLWTYDlw8jOC/Efgi8iYA3wXgORF5pt/tHwD4ThF5Am209KcAfM/xNQL4Ceg0omteFuGPVPLxkQFL7MP3KgiQK1fIDPzZTw/1kIwAeKSahi61OqjonzbqTaLvh8kYKs7PHwttfrbRAIzrc5h+uezDvhsPzY03KjTUmAipNorcj29g+ujLksBm1ZpLgCx6OsYnTjQ6l18CwwrwR0tMV+1su5Al3J6sjrIYMZXGdU5teoglRIu3PSaFlTJseW1lNWXvZDonqv8+ANvip5899yaJ8XlQB/XR5267YPqQ/aNV5DyGVVUYsHk/zvGA3QR8Yv9qljsu4kYA/dgIeNsu6LJeoIVVx7G1Hv3VdMbA+ondLXsrI2DTYPd1kvl7PQQ8fxrL3R+/z9Bw2zeOO1PyCzgLuS9zd9/IdEvw+3ZUNnH+yLuummDXJrA7+59lBGxiEavqA0plSU9AdxdzsF0ObImn93gDlUK0lkUZJAdAXIGeHk2W04XG6o+MT8zuLBDvZLPsB/mCdg0gmoXam1QTyNuGA5pY/5DxaR8gQBLjW1En/7HbgeKM3//MvuxCxzLolc5b/kT50Axw0DazfeXtYPy5374F80a5n7+Jl43mXK8mjwvFZwaDnuaXn4dfn5SoEVRwYNOPmA5RuRmRBvize8KKZS3/qYLRK3mVxTG7ZJAT8jVn1AE/HovCpfaWjYB3L8pD493TRYfsJsYXnpVl9vHBcl8OnowU4bKh2rbqtFQHPo1N5/fOJ5YPQyAAiqGd2N6DepXXEtZ/RHS/GjN+2PJge8/GDcyfDAAzfwrqGesfS3+7PpdpSFiqEzBQjgJ73D1Lqs6MZ39oq96k5CWXe5zAZmANSL6g5VemPLPxyset/IWvNaVg+xXjczmOGWP3NVSA/ZLPPpgkLp6vt62HMQAXZfwI5mXWTzOVMtsT6EMeAc7wjfKRrL/76B3cDoT5xZNK/dV16Ltmtg90we9fewNhA6AQoGpjd2N58/EdncBI7fPgEDIEEo84BY0G5g+wIwf1lKT+XkPqL6P6wfbtsYUeP9AkGEA/jbjkUZmSJH+Aa1W3VDStdHK5pCJcKABm/cGwBIEfyXs7Z3A3kX8P4zPU22CII3v5KZzbXYVE1vlaza+nZ0uPGgaCBWMqnxPpMsD3WpS0TMxUhrX09a794XSw5OiMPzO6yfkE7sWEk/ExyBiLX+0dTwcWs35MLVVE+siuZgBUmo8LtO8Y1v49w/aMc01YhSs9lk3d7SoCtEYHv4pnKYBuYIdH5/fO8Ptese+27sv1juvriv26DscrMUw3TNSKYt75zPTJVZvYnj56krr3miEAN9yhnsXzwEnXoNLhvIw2yjNLfwJ7kf4KrhEPGYu4iF862u/gQiZy0fk8VwZAzNLc20G6Fz2A2zX+CDnnamGETqQLTa89CblBnralVrRZdGt7B12K+vbw53TJA9BbAdcM+Hj9NIyCDVHl7TmjgX+ba117YzEDkIPzQqBn22HBGANvb9rdp/PGryzdCOyJ+aUvLRAXzI4I3u3B8gbs6+sd13vFvu/Y9xoG4LoZB6K1WJs/6nkubUldd+TPS/9OfNlQZFuDvoShTEY91bWmhp5lMwMjAnzjiW6qhPdvCvAhuZqpSOw2A/Mb6LFoi9AwVqNiEwI/P3x+VgP9CtEuj85Olwd+b6zQ1lWiifnVmb4KIHs3ABxYkcH+38D4AehhggqX/jGnHBsAAJ1pWfL3zd4QtAClYRKl78fzdnEs9PEJOKZdyrVLmzGwH7txMZlt9OLSj8uLVRPmgTrM9tcV1/veQW5GYGD8XZOachmuIIPgNDkE8QzkW2b7bUPu4pPo1i1WVgPDAd2oDw3eT/OSoD9Tr7OGrRkJJvXhoB/XeZvLwMuCUmb8hQK4wQjYNZJzq4EoAAAdRUlEQVSno+NtSBpicZyL48x0oS/pMHu0ha2ey9UOntpZvhZt4IfGJZaN45TUz0wf0n94GaXWZAgo89MttTcK7QM9SulrCNpwvA4elf4s4nLeZm0l09AqmQAPiAdskjFXpTILo1AN9C7zie3rKPE745vU3+fFvr1uTCdq3wu0DFpoqs+QUAqkbLmbtpABoKm1fC59WgKqDP7mYkg39DGPKsPZAKTkEpFBGNvLAPLYn5ke43auDro2f1UoQJ3aYRoohshzN+Y3gz43wCCM+REnyX+QbkHqC5Y+Pst9aWwsewvQiQwyDr2C3RCeZvyapP0a6CPz9wvnx7DDJQNftaAU+70ZKhGgSh8FZs/nTE8CUTqIE+ARtW8vfFMZmYNpBoBZv1aE1LfofZf718T0bAhC5rdFxMCvTYq7RCbGd3+ZvhNfRsBvwfgSk2xm5hcvM/d7vXzU3OBhhmuejWisq+z/cxOMVtifZAD4ejRfqARZwD70WlcZJ9ke0S4HxrcnlhG3k9BhyrDN+buFN6VbkfphjMdG3X3yXuBVGp014A+gp62bgZ/ZfmUAas0BP7s+r/z+2v16LS0Qierb6K6J1M5Wkg2bX84Z1ErH0EWA532+P0aDKQT2EfQE8L3iejcfv7H+aASqMX5RFDHD2+IVEDM8TdmYj5+l/hjQs+PtGjKAvhSB9mAqu0H8sNZ7M3NfBn0YDirsfmFvge6pGPhXoI+IPsgQuM0bbw8GtbXHsV3yebENHH9NZ2b7wcs3Vbg486Z0ceArFr59pbWggz/AJ2KyKECfDMEh8OPtsyzpaUYafuWWAoET21PSKtAiKAYCLUBpnfcWkxDRJpH7UoCQ+0BM5mH+MlUiO9mZ5QcDYozP5WhdeHtI/uzn78nP923y8UUURRUoDWwWpJQk8w0UxvhbjNV3A7DRNoM9DEDDVumgVfAX3lX758OM1Wia6+jbJzXogAov384BwoYm8A/gXq3t3Kk5c5tYgn327Tvqb0Rpan7eNlgmzJngM0Z1MqaLAP/qavftCG7E1M9tBFntr+jKvKZw+Yr5vaKTlLKKqBOol5J/kP6nxFMpBUUb8DcHXkGbd94ajfYuvm4bRPtgno5zdWy3SpYAvZABEPOBO/rbFSrSn4Dak9pQ28XbeHvI+X3vo/b2+M3OLSWYSdzoDMYpRbwZPCXyLTJsBz/ZM0b8w7z8+CoxZDG3fOQgKSYdfmceFHdL1gtPEKp1+B1IeR1TBIKtXWcDwK85p+2+X0RahHgAapE8Uj8sgaSHzYFoOn6DZbkI8K+vr307AmsbalXcqxX1XsVWawK7LIDPkM9bpAiUNEEHfu66y/urqH7V1RQKnTUAaFHoVlz2qhd6wS6tC7JIUy0NROogMnAy6KM9sZYctGVq7X1kUP/BxtAzqbTG1ddd/u/dwFYHfYB/HLnXlFYmqVXeGvhLGAE3VsmB6X8mw1WEykHg00xhaPRQutpBkzbZDKS/JJvTs7D24YW/hltZ2stQDURCmiflHBm+2vBpG0zFiqBSW1UuF/JLnNVzWfD9w7tRqqeb02WAfzUCvzSZfa+i1oJtr9ju7fSBxQx8md5eO2ECegPw5utsT+BOM+msDQEwu9e2WzZgU0A3AFqgiAE/IoK6V9Tu0zYGDSkoBlAHPQN8BfpFJhL4xZ9bVTzCzwN52Offd8X1wPg8lLc9ewe9fyqPWSY4cB7ZFizPiiDcAzgS2djF+DSGfBwH5AjuE9CPPLQA/Pqrviv/vj1XP2c0wD1ZZN7q11WnA3wBeI7yA+BenibwrNw0lzs/nN8LrnDt3uekizN+k/Ybai3Ya23Tbt2r2GrMymoGQIocAB9UAflBx8fOwCeg0/7KAPgtuL779qYKbCE128D8VhO1xyeSbLbRiB3wLp8lQDuzPsnkQ0s+G4eZ7Rdj83eS/YsXdVr3qfZeCWtv/T465M+lvAX7QiLPson5MtjWvjTcovetgfvxMzlsYvoxl/RuPQ7Abmw//u4XpaSgiULYzRwZneU/D4m2c3rwpzWHbmD7w5jWsU+0xzPyZ2F0uu456UI+fge+AFst3acv2GrBXndse/FBHjIA3nyvqREtbAGm0yTArez7K0aWTyP8qsa1+JrWoDvozQDQz6i7oIr4G3FWue0+PT+SZXmrcGJLBn3PQPo+qLcA6c9IwVIQ+Gtb74nxI9BnBiAttQPEXBMzUt2tCTNjGjmAEvnH9CxcJ+ThI5idJrtI28dGT8itoxrP59vtdTYA/mLOOB1YQZf7veyZ8Smp8ixBA8h7O2LjO776HS6g+IPGl8lMZfHT0DGql2hL3CN1Ol2I8SO4V7eKUgtKLah7m3Nv3wRl3wP0EgbAKgZgVkRuX0TLEYhpB1LXng6F70AnF6BXSAZ+vr9qk/oN9EIZ6r591UFlRLDMK2xiEAIUSeQkOZQ2/Z/4e9W8JMbvL+RwYG+vupiFB1BpjO9slBRHGKX038ieCeD0XLTvmBTpnx6LYF6I/jAMIAPByYP9Q5tPpkd60NDzimWAjw2A1z1nXfOa2R5eB1T3ifnjFemQ/72N9rYqTgDDk6we0O5LxqS+tIAfUr/ULuWrYC8FZY8IfgM5WeMB+Jl92z+uxCxw5P5j+9sR+OxbjYyfvhMHLK4d25EC+CKCvSrKBKag4jxE2eRsX2ggz5ItR/Aj/22wPRzEycenAJ/5+x7Vp7f02sAdkFpBlvgybItAWsUtlxREG7bzs3QG7UG+qYj7l3HlQNJ68ShNyNGDhtKVlQXkJvAz+0+R/ZntPbES09zG0oSlDviI6ifwj7cwRXcA+LYa1EXVZbms0kWA/+DBlW+XrQN/z/68TcCIJL/gFRIMnP3HDPiQx1apAXokAwAAeRrpYeot5IbK18z3i8h9vNbKFRvsy6P4OHWyQwZ6MOtxXdK4CMCNSpZ/tPSgX2UWAmDvTQSbMqjnCVAzMxYH/QiQWf7OS5O0VCeeJ77QsN+Z2KR2k8deiM3lqu0pWoC1oJQKi8eUUnt7i20bbKTWI1MqijS3LQYaRYUF8QfjTyDvKiste0UtbdnLDm0jwPpi1haQEmMb1O+pvubXqjUZ7uXXQ6d0IR8/gC97/rySUMEG8JH9rj4bbWJgkmHraKy4m5QblbHJQvJbA1QOBPH1Y3uv9gZhRa3SRrpNjXzd4FteRgMwy2AHP6jd951GCKOhaGsTy0dgY7XBQEsKgsV6MgAFSAspAFICrIjncogouMUR1o38dLtyBu/tQBE+t/QxVRUd/FpQagUKaHhxjbcJ+3Y1Q1DbNisAeyJNlWD1ObM9T1m+14qy13bPWlH2vRkaB73VXhv7HUOYuSzMQDb3VGuF7jEOxu53TroM4xPwyxhQIb8eCfBoIOZ90PGBhTMDxb7ZZk2NCsjAjwIegZ/UR/cFdxFID+JJFdTS5L2WCKhxkGdkf2v8PRuxZinDIEzI5zRI/dGNQL5nHfOAyEt6B38p1W9ePEtezjczvpWR1RGsxjx/KwMQBdLuq3RjykZ/e7LjHbUUFNUE+FIqzR+gHfzNoLfhyMH8/bFgJtLz6fmP+i61okoJ0A+Mv0tFKXtUhOVbKf8DSXG58pyJNjbjJQf8K+rHX7F5sDUcZNHg0FQAS20DpTF+mtmFvrfulWVQxwT8EfB2jvcoCJrFtxdz+n2sUovJuhJAXzN+SOuQjB1waowlFNwZmZySt7rOrjq+rnvE9jxyDMiAYgANS2d3WTH9uCARWCtnxHOvWH8EvP0VUr2NRSDxNx387GrwEKfG+P0Vau0jLyWDf1p3N6CWtWHTIVOJ8XuXblODBHip2EtneynY3f2QVOJNtfYyGQBv2zxvIoN+3yOQfipdlPH9obxtM2P3/ZKPH20z8HmOfn/1s/twVjluOT1X2a9kGdV8rMbu1XoZ7MWcIpBaILWiVEGtJfWb5+5CTGwPZkCA2k6W+uhlFdsgUMR5LM99BB9m0K9VRwbg9Pr0oKDg5TorK3a1vIRTeWB6YSkz/lAHwGAIcukY4MPUyGAjBaW9YtRGUFJeZAJ6Bn1rP5Wmga/t2VSbgR6M0KjuivaXtqqiCoGyy/1dKoq0AWtJ8fnTtaHfLu9J6psK8K8gEfj3uqPuLynGJx/fGBtw5g73cC3lJ8CXucHZBxsaYIsbgVxHmva5wbX/o9FJ7W+RaWP3oh302pmglhbB14jc5waQG0QCf2Qnp9SVI8QDo9q3Bih8pD9Tf5yHWQg+du8GquLGh40pu1XJNaA/t+sphudP4I9el+HpMuDHcqJkZDLeuxVY65IoGnlp858G0CX5+Uq+v00yUpMiVQWkv4MRbQZe6AZ+qfF3HHgz2b93P98/wmuK1mrdDI1fm9qnWq+NsX3I/ZcU47PUB5CAP5KbsXg2Cgd+JbFPms/d3xJrjH/UbnKDy+tmTCzaq82CaxuGu+/i4G9dgsUZ1dj/CPQZcH633qCogLhw+LBiOB7uwvLNx8WS5oHrj5xAT4xvoDf2776ag136OP1gezYi5t7o/NwJ/F4j88KWkjbDPnbDmJzjg3LTJvejXhcSP7WhGuNKvC0qVJsUH0lkkvo9AFx3xS4VIhVFatpmpg8DIO0FL26fZASi+86+j7Cj9mnUXlI+fspMr4RVTMbbjTN+bC8BrybxGzA9QiygoabHwF/oLN9u4+uNkTKQMihHkBBTC6/j2RJDwd7Yy0A3cIiMQBmW/kpzG6M/Ko/4W/A2VUKal918ZZ83jyfY4Ek0JU2dNU9NnWW3qtJoRY1pGKiO4JJ2ri1WYlxuvkX3jKJVP89x31VS7sKrbty3bWuAKm1g2VZL396wddneUkXVAulqQLkh2zNWRe3vUojskN3aRq7gWjbqltvCZ99qPMNIUIo2X2Ld+7yJHfh9/5x0zie0XgbgvwH4kn7+u1T1H4vI4wDeAeArAHwAwN9Q1evVNepQmQJjW4zkNVeiWXRuXGrb2hqnSsx/p03htXv0guqXXBuAPIes76i0IaE2MAb57xt+CPAJ6KFWAvQDg08GIHLD9jAx8sSWYf0Z7BFv6OegNfhQC3R1b4zdWprxoo+dsJqKKdHtI6crNcZ3IMaX/nyWt1RHQ+N2Y3AisX2Roag1baQ/a/muSdFtRVvvTAe81g21KLZN3RhEhQCqldqhhIFGlHsbRVdRd8GOCsHudeDnFGPuDXWvuLdtqFtnbpb6QNo34Ju8N9DX3y/gq+p9EflmVf09EdkAvE9EfhbAWwD8C1X9DyLyI2ifyP63B9fgHVKrilSzRKLCrSeBfZT5rTGJAb43fp+uafEFFJ/chm9t52nsV5VQEfSzzpntysSiv31/9H/dEMwyxxjJQOjKxe6ZQK/+HoB30VXEiDD/bWD+0crylqDPGKoTy8+MX5zx86fPhkL1AkdYYgOkMT8DX2g7/bFy1eQUtpaL03neRTmpm9oBr9oAr31pIC+oZUPZtL9XYuy/eXYURjLq7dIzog387dXuNpBghwAgQJI6qyVAX7fmOm57wdYZvxXJYABg71oQ4Ove38P4ffTxVfX3+ubL+t8ogG8G8B39+NsB/CMcAN/nqc8X9YewfymuhVG6SYoosyEw+dYuaYHS0g0B32NqlCNj2KbQhJ9oM+i0LFMFH14wgz9+ir9rE3PQiDtvqNHMnTHZ6DjLZ8bP48IX/fV2DbezHRRi9xT/Nxi/x05klv3JKNi8+tztJfEsBoRmyIjtRfpoO6qfUdIy1C0Klks71klwxbh8K0vmfhspqtX67rUBvUtvNwAd8LW23/15FFBRFCltghUiKW/N/UWv5h7Qj4o+3qMvpb+iXko2AHtI/RyibpVYTebXMAC175+TzgK+tPDtBwB8DYB/A+DXAfxfVUf0ZwD8kaO/Z8b3glHe4wq2eyKAI42B3adPrA8fCis+RRTiLScgs/6IRYgrQm9A6qQ0SNKpZA7ZHn5tAoK0cQDxkDlT3BO9NgCaAB8+/jAu3Jk/jICXduqyy+VghSADq+d58+k790l5mcrJosa7omQBfvAMO0esTxZrVG9kh91oWbU41ONZTY6XDngtiqJd6qtCC7Bt2lm++/VFsW1N+hujtPy37rrUtQwu6+pTleW5JbtxMYlvgC+FQF+yjw84+dgxY/gwALt36Z2TzmX8CuDrReQPAfiPAL72rKv3lHx8k/pUoSvgM+sDCJ++EPi1gdbGURvL29q/SJOuHczRIqjqDUWEjYbNl2fgjxCTptaWJb2BXwj8LvfHZyNVoORra78qlNpMYvrM+Mz01Y3B/DdHoHdMWTzFe0Uksfy8rME/psb4vaztPknqtwxIbx/ZAJxOYTvD1YgszHkRdKlfBdpH8m196Oy2tXH9HuSzAF9t0l9VoZt112kezutZVX+EOLR7XZXaFEaV9nmxBPhuBPbSJqdhph+3M9P3PvwXawCPqv6OiPwygDcC+HIRKd0ovBbAZ4/+7sPP/RfffuUrH8crX/W4W/qxD7c/2bBmia9Ifck9+r5i+1JOXRNocrcbFZj17lIxsSuBJ7F1kifR8CfmRz6WfAxwSw132EqFGNvKq4E8vxQ0fvK7vf+dFUsYLUQedHwUBvLqc1gm+ykAKPTeBSsfSgZ+deZnY6NBCB5r6RNtJuaPNbvVaTsVq7mCmfnbQKzSyUKhW3vDWrVF9pu07zK/+/61FmgHv6kFcweB6hbUYhbqMlGiR6N38xVpw4ZrV1IN8G2977UZhM3G7EcJctN1v94NQMVzH3kGH/7oh3BOOieq/5UArlT1/4nIHwDwlwD8IID/CuDbAbwTwHcDePfRNV7/p/9i7AyA134ss/4ohZEalbG+GQTrtx/l58z2q2sa8Id9vhe9vWXbPlio5DUHvMZ5BSZwLBky2NGbuoOZ5g8Y3sjid+/ziDm2HOFA+G1TOXVgLMA+f/qKo/38fJgWu7IZ1VHN+Rkd9DNPL9IAZjcMbER1uB7ZaVv8DTzhCH+BloLaA3za54dEV5IWEHR3oRuRDnNoV+k+UrAbN+2qtaLSBJ8FtQj2vSkAK9ets1a8cuVP1VRLrQH8vv0nXven8Lo//if9zJ9+908dFt85jP+HAby9+/kFwDtV9T0i8jEA7xCRfwrgGQA/fnSBbaNpahjkts1yfwVWgFi/JODwMR+6m5hnuFYC/gh+Bj6xmTCoR0NQThwbg18HgqDvR1PWgeUrzcwaMxPHOoZuKv2mvkRMIJeHSftBuUxgl8EA0PO6gRzWye2xa8PxLklmcLZYhhwlTds5NqLJbVmnUGhmsNoALbR+/a2g1OaHa1cA27a19qoK1Q2lKhmA0uIpXm/Whan99eAOeggEre9/73ulCPYi2KRgt9GCvWxHluf9mBq+gd6JYBVIX6RzuvOeA/BnFsd/A8BfOOcmR8AfWX+sbJ6cIAeQRuCzQRgizEC6rnKjEkIf0VM0YJ777wjoMhyjLq6puysrgEkRC+WxbQAAfdNvBj2/jplA77MKeU86MmDQQRjlaM8e+SfG30b2H1SAqy+ukwC4GVQvd3tc+32K85jDk1oE1snIgxSFWbmFAQgDHG2nlDYC09/S23rgr4PeAL9V9FmWWxzA3+iDwgbiOZkpWr1ZvlI1N0tfSrzxacOEvQ76s82cpchfgOI6fwmN3Nu2kvZPynxY9Q4KoAOewd4OB+izIcjAt3uE1DyQ9rzvfixL+xHo2TjIWQbAyFWc+fnp1ejf/GFje17qEfvnj4fMch9u6FYGFA6EebReYn43YidG8PE27Fl7g7ZnZ4BLBvtpyW/GcTDuPVbgda+aCjiaU6+H0t6AhxZoqV3mW9/+5n696taCppuidMZvysCkvnY2D1Kz6ddaVt0qUPY1Gcu8nkWqkCsUn3ivAzHcHBAFLgZ8YvwkhzABfzICXK8L+RjGgFwAknHtGvmVTTMEM+gJ+OSjj8Beg58BH7PWFBlZUBIgsirpsrVHvBPj2yfAxhldeCHpn2cXMr4fYykIMBJoV9+4W/r69DHM1XMKNV1xkFu9UR58I8+ff17SvJkAz9eSBCQzvjYPhBagoLjvbv36qlsEd6tC69YCe6WgfSnIpH5NXbUeGK7VA32qpgR6MFPh5VLcKFIb7A/lV3VjwDGcqGf/CtQZ6XaAP/j1HuGfmJ9Zn5kjWCNLVQZWF5AD6J35Q++dYHwaojr47ofgp/kAysiGJa5Pju5Abcb4ZAw1ZHs1ua8Z8GYQlJUA+/dD2Sa5W/Izz2x/Ktgnh8+Y1s74nAeG4uojJvEblw9v+ujGDqLGvOgDhMaytfYDH6vhBqC0+zRGt359hQ3n1dLYvs3BUCL672oQEJvoshNb6nlJPTAxm7MHlq1OemtNTSOxf0iGMYirqKndnErl5lO++LRtBdtW8Pzzv9bm0d82bPe2tF22Evtb3i+l/X378mqPfm6ZZdNc/C79M6Pz/m9/5hMEwiPAz3K93Aj+LP+T1JcBGGgV/PST7xtKLPi5Dhad38HeJ7a3883n74yAYBgArB2z4SsFv/npj0+At/1tm48nQyH8jIOq8cYbag2UnSDLOP7kr7yXThjLZ7Gb7AMd1HxS8IcpnKxkNn/eaI+/+uz/yO1zNITczpAZ3yPw+479+rp9tPTqGtfX17i6usLV1RUeXF3hwYMrPHjwAPcfPMD9+7bc9+XB/fu4f/8BPvbx53D//gM8eBDL1dUDXD24wtXVtS+n0kWAbwXyuc9+8sRJx0uKzi+unb0h2j7+M3zutz6BfIIM68ul9z/13lu46zp9+lMfm46dKP4vOskIaqr3J598721UxzJ98APvn46dk7VzHZb5fAoI5BV+/Tc+0fYf9uKULgL8u3SX7tJLK90B/wWmF5MF79JderGTnBsFfME3kPHNirt0l+7SpZLy/GyUXnTg36W7dJdeeulO6t+lu/QIpjvg36W79AimiwFfRN4sIv9TRD4hIj9wqfse5OVTIvKrIvKMiPz3C9/7x0Xk8yLyITr2ChH5eRH5uIj8nIh82S3m5a0i8hkR+WBf3nyBfLxWRH5JRD4iIs+JyN/txy9eLou8fF8/fhvl8jIRebq30+dE5K39+OMi8lTH0k+JyMMPxJtG/7wIC5qB+TUAXw3gMQDPAvjaS9z7ID//C8Arbune3wTgCQAfomP/HMDf69s/AOAHbzEvbwXwlguXyasBPNG3Xw7g42iTvVy8XE7k5eLl0vPwpX29AXgK7cW4dwL49n78RwB8z8Ne91KM/wYAn1TVT6vqFdrsvN96oXuvkuCW3BxVfS+A/zMc/la0eQvR13/1FvMCXHjYjKo+r6rP9u3fBfAxtMldLl4uB3l5Tf/54p24ejzf5U/3428H8Nce9rqXavyvAfBbtP8ZRGHeRlIAPyci7xeRv3OL+bD0KlX9PNAaHoBX3XJ+vldEnhWRH7uU22FJRB5HUyFPAfiq2ywXysvT/dDFy0VEiog8A+B5AL+Ah5zv8ig9qsG9N6nqnwPwV9Aq85tuO0NDus0+1h8G8DWq+gRaY3vbpW4sIi8H8C4A39/Z9twX8i+Rl1spF1Wtqvr1aAroDXjI+S6P0qWA/1kAf5T2T87R92InVf3tvv4C2uShb7itvPT0eRH5KgAQkVcD+N+3lRFV/YJ25xHAjwL485e4bw9QvQvAT6qqTeN2K+WyysttlYslVf0dAL8Mmu+y//SCsHQp4L8fwOtE5KtF5EsA/HUAP3Ohe6ckIl/arTlE5A8C+MsAPnzpbCD7iz8D4G/27e/GifkLX+y8dIBZ+jZcrmx+AsBHVfWH6NhtlcuUl9soFxH5SnMpJOa7/ChivkvghZbLBaOTb0aLkH4SwN+/dHSU8vHH0HoVngHw3KXzAuDfA/gcgPsAfhPA3wLwCgC/2Mvn5wF8+S3m5d8B+FAvo/+E5me/2Pl4E9qnZqxePtjby1dculxO5OU2yuX1/f7P9nv/Q2rDTwP4BFqE/7GHvfbdkN27dJcewfSoBvfu0l16pNMd8O/SXXoE0x3w79JdegTTHfDv0l16BNMd8O/SXXoE0x3w79JdegTTHfDv0l16BNMd8O/SXXoE0/8HtBTloEsCk4QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7c22099f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "random_int = np.random.randint(0,train_images_array.shape[0])\n",
    "image = train_images_array[random_int,:]\n",
    "image = np.reshape(image, [32,32,3])\n",
    "label = train_labels_array[random_int]\n",
    "print(label)\n",
    "plt.imshow(image)\n",
    "plt.show()\n",
    "\n",
    "random_int = np.random.randint(0,test_images_array.shape[0])\n",
    "image = test_images_array[random_int,:]\n",
    "image = np.reshape(image, [32,32,3])\n",
    "label = test_labels_array[random_int]\n",
    "print(label)\n",
    "plt.imshow(image)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it is necessary to create a **TFRecord** file that we can easily use in Tensorflow. We can do it using the following function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def numpy_to_tfrecord(images_array, labels_array, output_file):\n",
    "    with tf.python_io.TFRecordWriter(output_file) as record_writer:\n",
    "            for i in range(labels_array.shape[0]):\n",
    "                #Getting the data as train feature\n",
    "                bytes_feature = tf.train.Feature(bytes_list=tf.train.BytesList(value=[images_array[i].tobytes()]))\n",
    "                int64_feature = tf.train.Feature(int64_list=tf.train.Int64List(value=[labels_array[i]]))\n",
    "                #Stuff the data in an Example buffer\n",
    "                example = tf.train.Example(features=tf.train.Features(feature={'image': bytes_feature,\n",
    "                                                                               'label': int64_feature}))\n",
    "                #Serialize example to string and write in tfrecords\n",
    "                record_writer.write(example.SerializeToString())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "numpy_to_tfrecord(test_images_array, test_labels_array, \"./svhn_test.tfrecord\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "numpy_to_tfrecord(train_images_array, train_labels_array, \"./svhn_train.tfrecord\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From TFRecors to Tensorflow Dataset\n",
    "-----------------------------------------\n",
    "\n",
    "The two TFRecord files are ready to be used. Like for the CIFAR-10 tutorial we are gonna use the class `tf.data.TFRecordDataset` in order to manipulate the images. Once you have a Dataset object, you can transform it into a new Dataset by chaining method calls on the `tf.data.Dataset` object. For example, you can apply per-element transformations such as `Dataset.map()` (to apply a function to each element), and multi-element transformations such as `Dataset.batch()`.\n",
    "\n",
    "The `Dataset.map()` method takes as input a **parse function** that takes each single image and labels and adjust them. Here the image is converted back to `uint8` data type (because TFRecord stored the image as string) and the label is converted from a single integer representing the class to a one-hot vector that can be used in classification models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading the datasets...\n",
      "Parsing the datasets...\n",
      "Verifying types and shapes...\n",
      "(tf.uint8, tf.float32)\n",
      "(TensorShape([Dimension(32), Dimension(32), Dimension(3)]), TensorShape([Dimension(10)]))\n"
     ]
    }
   ],
   "source": [
    "def _parse_function(example_proto):\n",
    "    features = {\"image\": tf.FixedLenFeature((), tf.string, default_value=\"\"),\n",
    "                \"label\": tf.FixedLenFeature((), tf.int64, default_value=0)}\n",
    "    parsed_features = tf.parse_single_example(example_proto, features)\n",
    "    image_decoded = tf.decode_raw(parsed_features[\"image\"], tf.uint8) #char -> uint8\n",
    "    image_reshaped = tf.reshape(image_decoded, [32, 32, 3])\n",
    "    label_one_hot = tf.one_hot(parsed_features[\"label\"], depth=10)\n",
    "    return image_reshaped, label_one_hot\n",
    "\n",
    "print \"Loading the datasets...\"\n",
    "tf_train_dataset = tf.data.TFRecordDataset(\"./svhn_train.tfrecord\")\n",
    "print \"Parsing the datasets...\"\n",
    "tf_train_dataset = tf_train_dataset.map(_parse_function)\n",
    "print \"Verifying types and shapes...\"\n",
    "print(tf_train_dataset.output_types)\n",
    "print(tf_train_dataset.output_shapes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the Tensorflow Dataset in a session\n",
    "-------------------------------------------------\n",
    "\n",
    "Now the tensorflow datasets have been created and they are ready to be used in a session. The most common way to consume values from a Dataset is to make an **iterator** object that provides access to one element of the dataset at a time. A **one-shot iterator** is the simplest form of iterator, which only supports iterating once through a dataset, with no need for explicit initialization. Here I define a tensorflow iterator that can be used in our session, the size of the bacth, and the number of epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.name_scope('dataset'):\n",
    "    batch_size = 32\n",
    "    num_epochs = 5\n",
    "    tf_train_dataset = tf_train_dataset.batch(batch_size)\n",
    "    tf_train_dataset = tf_train_dataset.repeat(num_epochs)\n",
    "    iterator = tf_train_dataset.make_one_shot_iterator()\n",
    "    next_batch_imgs, next_batch_labels = iterator.get_next()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Iterator.get_next()` method returns one or more `tf.Tensor` objects that correspond to the symbolic next element of an iterator. If the iterator reaches the end of the dataset, executing the `Iterator.get_next()` operation will raise a `tf.errors.OutOfRangeError`. After this point the iterator will be in an unusable state, and you must initialize it again if you want to use it further. A common pattern is to wrap the \"training loop\" in a try-except block:\n",
    "\n",
    "```\n",
    "while True:\n",
    "  try:\n",
    "    sess.run(result)\n",
    "  except tf.errors.OutOfRangeError:\n",
    "    break\n",
    "```\n",
    "Now I define a new session and then I ask for a new batch of images and labels. Every time that the `sess.run()` is called a new batch is returned..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features, labels = sess.run([next_batch_imgs, next_batch_labels])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can sample a random image and the one-hot label from the batch, and visualize them through matplotlib..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label value: 6\n",
      "One-hot-vector: [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
      "Batch shape: (32, 32, 32, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7c2202fdd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "random_int = np.random.randint(0, 32)\n",
    "image = features[random_int,:,:,:]\n",
    "label = labels[random_int]\n",
    "print \"Label value: \" + str(np.argmax(label))\n",
    "print \"One-hot-vector: \" + str(label)\n",
    "print \"Batch shape: \" + str(features.shape)\n",
    "plt.imshow(image)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
